Multifaceted radiation detection and classification system

ABSTRACT

A system identifying a source of radiation is provided. The system includes a radiation source detector and a radiation source identifier. The radiation source detector receives measurements of radiation; for one or more sources, generates a detection metric indicating whether that source is present in the measurements; and evaluates the detection metrics to detect whether a source is present in the measurements. When the presence of a source in the measurements is detected, the radiation source identifier for one or more sources, generates an identification metric indicating whether that source is present in the measurements; generates a null-hypothesis metric indicating whether no source is present in the measurements; evaluates the one or more identification metrics and the null-hypothesis metric to identify the source, if any, that is present in the measurements.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent document claims benefit of priority of U.S. ProvisionalPatent Applications No. 62/673,750, entitled “Radiation DetectionAnalysis Kit (RDAK)” and filed on May 18, 2018, and No. 62/805,825entitled “Radiation Detection Analysis Kit (RDAK)” and filed on Feb. 14,2019, which are both hereby incorporated by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

The United States Government has rights in this invention pursuant toContract No. DE-AC52-07NA27344 between the U.S. Department of Energy andLawrence Livermore National Security, LLC, for the operation of LawrenceLivermore National Laboratory.

BACKGROUND

The detection of sources of radiation is particularly important to helpsecure nuclear material and to ensure the safety of the generalpopulation, military personnel, first responders, and so on. Sources ofradiation may be grouped into radiation source classes based on the useof the radiation. For example, a radiological dispersal device (e.g.,dirty bomb) and a radiopharmaceutical (e.g., ^(99m)Tc in a medicalpatient) would be in different radiation source classes. The radiationsource classes of many sources of radiation (e.g., medical patients)represent approved uses because any safety risk is deemed acceptable,and the radiation source classes of other sources of radiation (e.g.,dirty bombs) represent not approved uses because the safety risk isdeemed unacceptable. Because some uses are approved and other areunapproved, it is rarely useful to simply detect the presence of asource of radiation. Thus, one of the goals of detection of sources ofradiation is to differentiate radiation source classes that may presentan unacceptable safety risk from those that do not.

To support radiation detection, a radiation detector, also referred toas a detector, (i.e., a physical device that detect photons) is used tocollect counts of photons with energy within the radiation spectrum. Aradiation detector is only able to identify the energy of a photon to acertain level of accuracy. As a result, the radiation detector may groupcounts of photon into ranges of energy. A radiation detector may collectthe counts over a time period (e.g., one second) and present the countswithin each energy range as a measurement of radiation (i.e., aradiation spectrum).

To detect sources of radiation over a wide area (e.g., city), a dronewith a radiation detector may fly over the area in a pattern to collectmeasurements. Detectors can also be driven in vehicles, hand carried, orplaced at strategic stationary locations. Those measurements can beprovided to an analysis system to identify whether there is a source ofradiation of interest which may be a source of radiation that presentsan acceptable or unacceptable safety risk. Because different areas canhave very different background radiations (e.g., caused by differentgeological formations or building materials), the measurementsrepresenting the same source of radiation in different areas can be verydifferent. As a result, such an analysis system needs to consider thebackground radiation present in the area from which the measurements arecollected.

The speed and accuracy at which a source of radiation is detected andclassified (e.g., as an unacceptable or acceptable safety risk) isimportant. In addition, it is important to identify the nuclide (e.g., Uor ¹³⁷Cs) that is the source of radiation. However, some of the mostadvanced analysis systems require vast amounts of computationalresources to process measurements, identify the background radiation,identify the source of radiation, and classify the radiation sourceclass of the source of radiation. In addition, many of the currentlydeployed analysis systems employ detection algorithms that are notparticularly accurate. Thus, such analysis systems may be impractical touse because of the needed computational resources or because of the pooraccuracy. There is a strong need for algorithms that are bothcomputationally efficient and have a high accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that illustrates the overall architecture ofthe RDA system in some embodiments.

FIG. 2 is a block diagram that illustrates components of the radiationsource detector of the RDA system in some embodiments.

FIG. 3 is a block diagram that illustrates components of the radiationsource identifier of the RDA system in some embodiments.

FIG. 4 is a graph that illustrates detection efficiencies.

FIG. 5 illustrates calibrations of measurement based on a new binningstructure.

FIG. 6 is a flow diagram that illustrates high-level processing of anOSP match filter component of the RDA system in some embodiments.

FIG. 7 is a flow diagram that illustrates the process of a calibratecomponent of the RDA system in some embodiments.

FIG. 8 is a flow diagram that illustrates the processing of a split bincomponent of the RDA system in some embodiments.

FIG. 9 is a flow diagram that illustrates the processing of an estimatebackground based on rank average component of the RDA system in someembodiments.

DETAILED DESCRIPTION

A method and system for detection and classification of sources ofradiation is provided. A radiation detection and analysis (“RDA”) systemprocesses a stream or sequence of measurements of radiation (i.e.,gamma-ray spectral data and neutron measurements) and analyzes themeasurements in real time to detect presence of a source of radiation,identify the source of the radiation (e.g., U or ¹³⁷Cs), and determinethe radiation source class of the source of radiation. The RDA systemprovides a radiation source detector component (“radiation sourcedetector”) that employs a number of detection algorithms and can operateon a wide range of detector types with different materials and detectorsizes. Each detection algorithm produces one or more detection metricsupdated with each new measurement in the stream. The detection metricsoptimize the signal-to-noise ratio under various conditions. Theradiation source detector employs various background estimationalgorithms to estimate the background radiation (“background”) so thatthe contribution of the source of radiation to the measurement can bedetermined. The radiation source detector continually retrains thebackground estimation algorithms based on background measurements (i.e.,measurements with no source detected). The RDA system also includes aradiation source identifier component (“radiation source identifier”)that, when a source of radiation is detected, identifies the source ofradiation (e.g., ¹³⁷Cs) and its radiation source class. When a radiationsource class is identified that is of interest (which may be an approvedor unapproved use), the RDA system may output an alarm indicating theradiation source class.

The RDA system can be used in stationary or mobile detectionapplications. Not all of the algorithms provided by the RDA system aresuitable for all detection applications. The RDA system provides anarchitecture that allows different combinations of detection algorithmsand background estimation algorithms to be selected to optimizeperformance. Various combinations of algorithms can be used on samplemeasurements (actual or simulated). For each application, a combinationof algorithms can be selected that is most appropriate for thatapplication. The most appropriate combination can then be used in theRDA system to operate in real time with enhanced accuracy. Moreover, theRDA system can be used to process previously collected measurements muchfaster than if those measurements were processed in real time.

FIG. 1 is a block diagram that illustrates the overall architecture ofthe RDA system in some embodiments. The RDA system 100 includes aradiation source detector 110 and a radiation source identifier 120.

The radiation source detector inputs measurements and outputs a sourcedetected flag for each measurement (or collection of measurements)indicating whether a source might be present in that measurement. Theradiation source detector includes a calibrate measurements component111, a calculate metrics component 112, an evaluate metric component113, an estimate background component 114, and a detection sourcedefinitions store 115. The calibrate measurements component calibratesmeasurements to account for drift and nonlinearities in the sensors of adetector. The calculate metrics component calculates metrics for ameasurement to indicate whether any source as defined by a sourcedefinition might be present in the measurement. A source definition fora source includes a source signature (e.g., spectral shape) for thatsource factoring in a specified shielding such as shield type and shieldthickness. Thus, a source of radiation may be associated with a sourcedefinition for each specified shielding. A source definition can includemore than one source. Some algorithms operate without a sourcedefinition detecting any anomaly different from background. The evaluatemetrics component sets the source detection flag based on an evaluationof the metrics, either individually or in aggregate, to determinewhether a source of radiation may be present in a measurement. Theestimate background component updates an estimated background based onthe measurements for which a source of radiation was not detected asbeing present. The calculate metrics component factors in the estimatedbackground when calculating a metric. The detection source definitionsstore includes a source definition that defines source signatures ofsources radiation that are to be detected.

The radiation source identifier identifies the source type and itsradiation source class when the radiation source detector detects that asource is present. The radiation source identifier 120 includes acalculate metrics component 121, an identify source component 122, andan identification source definitions store 123. The calculate metricscomponent calculates metrics in a manner similar to that of thecalculate metrics component of the radiation source detector but may usean algorithm that is particularly effective at identifying sources. Theidentify source component identifies the source based on evaluation ofthe metrics. The identify source component may output a probability foreach radiation source class. The identification source definitions storeincludes source definitions for sources to be identified. Theidentification source definitions store includes a more comprehensiveset of source definitions than those of the detection source definitionsstore to improve the accuracy of the source identification.

The RDA system inputs spectral measurements that are represented as aspectral histogram representing a detection energy range (or spectrum)with a fixed number of energy bins that each represents an energy rangewithin the detection energy range. For example, if the energy range is100 keV to 1 MeV, then the histogram may have 50 energy bins that eachrepresents an energy range of 20 keV. The first energy bin would have anenergy range from 100 keV to 120 keV, the second energy bin would havean energy range from 120 keV to 140 keV, . . . , and 980 keV to 1 MeV.The histogram has energy edges of 100 keV, 120 keV, 140 keV, . . . , and1 MeV. Thus, there is one more energy edge than the number of energybins. For a measurement interval (e.g., one second), each energy bincontains the count of photons detected that was within the energy rangeof that energy bin.

The table below provides a description of terminology used to describethe RDA system.

Terms Term Definition Source (N_(i)) An emitter of photons (e.g., ¹³⁷Cs)Detector A device that collects photons and records counts and energylevels of the photons. Energy Energies between a minimum and maximumenergy. range Measurement Measurement range of a measurement. range Binrange A subdivision of the measurement range also referred to as a bin.Measurement A histogram containing for photon count for each bin in(X_(t)) the measurement range for one measurement interval (t); alsoreferred to as a spectrum. Source A combination of a source signaturefor a source, definition (SD_(i)) shielding, and an identification ofthe source. Source A histogram representing a source of interest with asignature (S_(i)) shielding. Shielding Specification of the shielding(e.g., type and thickness) between a source and detector (e.g., noshielding, 0.5 cm of lead, and 1.0 cm of concrete) Detection Analgorithm that generates a metric indicating whether a algorithmmeasurement represents the presence of a source other than background.Window A collection of one or more sequential measurements, which mayalso be referred to as a time window or energy window.

FIG. 2 is a block diagram that illustrates components of the radiationsource detector of the RDA system in some embodiments in more detailthan 110 in FIG. 1. The radiation source detector 200 inputsmeasurements, detects whether a source may be present in a measurement,and outputs a source detected flag. The radiation source detector,however, does not perform the more computationally expensive process ofactually identifying the source. The radiation source detector includesa calibrate measurement component 201, generate metric components 202,an aggregate metrics component 203, an evaluate metrics component 204,train metric components 205, a veto component 206, estimate backgroundcomponents 207, and a generate calibration component 208. The radiationsource detector also includes a detection source definitions store 211and a history store 212. Each component may also include a store forstoring data locally that is generated by the component or received fromother components.

The calibrate measurements component generates calibrated measurementsto account for drift and nonlinearities in the measurements of adetector using a rebinning process. The calibrate measurements componentinputs measurements and calibration data and outputs calibratedmeasurements. The calibration data indicates how to rebin themeasurements based on the drift of the detector.

The generate metric components may include generate metric componentsfor various types of detection algorithms. A generate metric componentinputs a calibrated measurement, optionally navigation information(e.g., GPS information to identify the presence of a building), and setsof coefficients for source signatures and generates a metric for eachsource signature indicating whether the measurement (i.e., calibrated)represents the presence of the source of that source signature, or thepresence of any source that is not background. Each set of coefficientsis associated with a combination of a source signature and a detectionalgorithm. For example, a first set of coefficients may be the sourcesignature for ¹³⁷Cs without shielding and an orthonormal subspaceprojection (“OSP”) matched filter (described below), a second set ofcoefficients may be for the source signature for ¹³⁷Cs with shielding of10 cm of lead and the OSP matched filter, and a third set ofcoefficients may be for the source signature for U without shielding andan likelihood ratio test (“LRT”) matched filter (described below). Foreach set of coefficients, the generate metric component for theassociated detection algorithm uses the coefficients to generate ametric as semi-definitive indication that the source represented by asource signature is present. The radiation source identifier provides amore definitive indication for each source. Continuing with the example,the detection algorithm for a matched filter applies the set ofcoefficients for the source signature of ¹³⁷Cs without shielding togenerate a first metric and applies the set of coefficients for thesource signature of ¹³⁷Cs with shielding of 10 cm of lead to generate asecond metric. Each metric may be represented as a numerator and adenominator pair, referred to as metric partials. For example, themetrics for the source signature of ¹³⁷Cs without shielding for acertain detection algorithm may be (1,5). Although the detectionalgorithms are primarily described as being matched filters, thedetection algorithms may also be anomaly detectors. An anomaly detectorgenerates a metric indicating whether the deviation of a measurementfrom the estimated background may be sufficient to indicate that asource might be present irrespective of what the source might be.

The aggregate metrics component generates an aggregated metric for eachcombination of a source signature, a detection algorithm, and a windowof measurements (e.g., rolling window). Each window includes a number ofthe most recent measurements such as 1, 2, and 4. For example, themetric partials for the source signature of ¹³⁷Cs without shielding andthe certain detection algorithm for four sequential measurements may be(1,4), (3,5), (1,2), and (3,5). To aggregate the metrics for a windowfor a source signature, the aggregate metrics component may sum thenumerators and sum the denominators of the metric partials and set theaggregated metric to the sum of the numerators divided by the squareroot of the sum of the denominators. Continuing with the example, theindividual metric partials (1,5) would provide for the time window of 1:the metric of 1 divided by the square root of 4, which is 0.5. The firsttwo metric partials would be aggregated for the time window of 2: thesum of the numerators is 4, the sum of the denominators is 9, and 4divided by the square root of 9 is 1.33. All four metric partials wouldbe aggregated for the time window of 4: the sum of numerators is 8, thesum of the denominators is 16, and 8 divided by the square root of 16gives an aggregated metric of 2. So, the aggregated metrics are 0.5,1.33, and 2 for ¹³⁷Cs without shielding and the certain detectionalgorithm for windows with one, two, and four measurements. Aggregationcan also occur based on measurements taken in the same location even ifat different times.

The evaluate metrics component inputs the aggregated metrics and outputsa single decision metric indicating whether a source was detected. Theevaluate metrics component first normalizes each aggregated metric bysubtracting the measured mean and dividing by the measured standarddeviation. The decision metric is then determined from an evaluation ofall the normalized aggregated metrics including different time windows,different algorithms, and different sources. This evaluation typicallyuses the maximum of each normalized aggregated metric; weighting factorscan also be used either determined based on performance analysis ofdifferent methods, or using machine learning as detailed below.

The evaluate metrics component then compares the decision metric to anumber of thresholds that are established to control the operation ofthe radiation source detector. One such threshold is the source presentthreshold. The evaluate metrics component sets a source detected flag(i.e., true or false) when the decision metric exceeds a source presentthreshold. The source detected flag will then trigger the radiationsource identifier. The source present threshold may be set so that afailure of detecting a source when no source is present (false positiveor false alarm) is likely to occur at a certain rate such as one falsealarm occurs per 8 hours. The certain rate may be based on the period(e.g., 8 hours), the measurement rate, the number of rolling windows,and number of metrics. As another alternative, the evaluate metricscomponent may calculate the decision metric using a classifier generatedvia machine learning algorithm (e.g., support vector machine, Bayesianclassifier, and neural network). The classifier may be trained usingtraining data that includes feature vectors and labels. The features ofthe feature vectors may include a detection algorithm, a source, awindow size, and an aggregated metric. The feature vectors may begenerated based on data collected and generated while performing actualdetection involving different sources. The feature vector may also begenerated using mathematical models to generate simulated measurementsfor different types of sources such as ¹³⁷Cs with a shielding of 10 cmof iron, different angular velocities between a source and a detector(e.g., identified based on GPS readings), and so on. The labels may bean indication of whether a feature vector represents the presence (e.g.,true or false) or a probability of the presence of the source. Thelabels may be manually generated or generated based on data generatedwhen the radiation source identifier identifies a radiation source and aradiation source class.

The evaluate metrics component also compares the decision metric to anextended aggregation threshold. When this threshold is exceeded, theradiation source identifier is triggered to aggregate until the decisionmetric falls below the end aggregation threshold. The decision metricmay also be compared with a veto threshold, typically set very low. Whenthe decision metric exceeds the veto threshold, a lockout flag is sentto the veto component 206 that controls whether or not a measurementwill be used for calibration or background estimation.

The estimate background components may include an estimate backgroundcomponent for each background algorithm. An estimate backgroundcomponent inputs measurements and generates background statistics for anestimate of the background. Each background algorithm may generate adifferent statistic such as average counts in a measurement representingbackground (referred to as rate), an average measurement for thebackground, or a basis for the background. The detection algorithms mayemploy different background statistics. For example, one detectionalgorithm may employ a rate statistic, and another detection algorithmmay employ rate and basis statistics. The estimate background componentsmay generate statistics that are a weighted sum of a new statistic andthe previous statistic. For example, if the new rate is 160 and theprevious rate was 200 and the weight is 0.25, then the weighted rate tobe used as the current rate would be 190 (i.e., 200*0.75+160*0.25). Theweight thus controls how quickly the estimates adjust based on newstatistics. The estimate background components may generate thebackground statistics periodically (e.g., every 5 minutes) or based onan analysis that the current background is significantly different fromthe previous background.

The veto component provides uncalibrated measurements to the generatecalibrate measurement component and calibrated measurements to theestimate background components. The veto component, however, filters outthose measurements that should not be used in an estimate of thebackground. For example, each measurement that was used in generating anaggregated metric that is above a veto threshold may be filtered out.The measurements before and after those measurements may have acontribution of the source. To remove this contribution from thebackground, the veto component may filter out measurements that arewithin an expanded window (fixed or variable) before and after themeasurements used to generate the aggregated metric. Thus, the use of anexpanded window allows for measurements to be filtered out that may beaffected by the source but not included in the aggregated metric. If notfiltered out, those measurements would be factored into the calculationof the background, resulting in an estimated background that would tendto become less sensitive to the source. The veto threshold may be set sothat a missed veto is likely to occur at a certain rate such as onemissed veto every 10 minutes. The certain rate may be based on theperiod (e.g., 10 minutes) and the measurement rate.

The train metrics components inputs source signatures and backgroundstatistics and generates sets of coefficients for combinations of sourcesignatures and detection algorithms. A set of coefficients, however, maynot be generated for each combination. For example, one detectionalgorithm may be particularly effective for identifying ¹³⁷Cs but noteffective for identifying U. In such a case, that detection algorithmmay generate a set of coefficients for ¹³⁷Cs, but not for U.

The history store contains each measurement and calibrated measurement.The detection source definitions store contains source definitions(e.g., one for each shielding) for each of each source whose presence isto be detected.

FIG. 3 is a block diagram that illustrates components of the radiationsource identifier of the RDA system in some embodiments. When theradiation source detector detects that a source may be present, theradiation source identifier executes and identifies the source (e.g.,¹³⁷Cs) and the radiation source class that is the predominantcontributor to the non-background photons measured. The radiation sourceidentifier inputs the measurements and background statistics used by theradiation source detector to detect that a source is present. Themeasurements may be a summation of the measurements included in eachwindow. For example, if the window includes 512 measurements, theradiation source identifier adds those measurements together to give asingle summed measurement. The radiation source identifier outputs anindication of the source that is present. The radiation sourceidentifier may output for each source a probability that that source ispresent. For example, the radiation source identifier may output aprobability of 0.67 for ¹³⁷Cs and a probability of 0.11 of U. Theradiation source identifier also applies radiation source class rules toidentify the radiation source class of the source. For example, theradiation source class of the source may be medical, industrial,fissile, and so on. The radiation source classes may also be representedby probabilities. The radiation source identifier may include acalibrate measurements component 313, generate metric components 301,train metric components 302, a select source metrics component 303, agenerate null-hypothesis metric component 304, a generate source scorescomponent 305, and an identify radiation source class component 306. Theradiation source identifier also includes an identification sourcedefinitions store 311 and a radiation source class rules store 312.

The calibrate metrics component of the source classifier inputsmeasurements used by the radiation source detector when a source isdetected. The calibrate metrics component aligns the bin ranges of themeasurements and the source signatures. The calibrate metric componentmay shift either the bin ranges of the measurements or the bin ranges ofthe source signatures. The shifting of the bin ranges of themeasurements induces correlations between the bins that need to betracked to ensure statistical accuracy. The shifting of the sourcedefinitions is more statistically accurate but takes more computationalresources because each source signature needs to be shifted.

The identification source definitions store is similar to, but has amore comprehensive set of source definitions than, the detection sourcedefinitions store. The set is more comprehensive to account for thedetection efficiency of a source definition. The detection efficiencyrefers to the likelihood that the source signature will be effective indetecting the source irrespective of the shielding. For example, asource signature for a source with a shielding of 5 cm of lead may havea detection efficiency of 0.25 and with a shielding of 7 cm of lead mayhave a detection efficiency of 0.15. The goal is to have sourcesignatures for enough shielding configurations to minimize the shieldingconfigurations for which the detection efficiency is less the maximumpossible detection efficiency. To maximize the overall performance, atradeoff is required between detection efficiency across all shieldingconfigurations on interest, and false alarm opportunities which willincrease with the number of signatures in use.

In some embodiments, it is useful to capture signatures observed in thefield. Using a form of semi-supervised learning, significant detectionswith spectra that have similarity to a source signature, but is not asclose a match as expected, can be extracted from the measurement tocreate a new source signature that can be added to the source signaturestore. To determine if a measured spectrum is adequately represented bythe source signatures in the source signature store, the detectionefficiency with the extracted spectrum is compared with the detectionefficiency of the existing source signatures in the source signaturestore. This method can increase sensitivity to sources being tested thatare in between shielding configurations used in the source signaturestore, unexpected mixtures of sources, or have some differences from thesource signatures in the source signature store. This new sourcesignature can be added to the source signature store locally for thedetector in use, and when desired also distributed to source signaturestores in use by other detectors of the same type.

For identification, the source is assumed to already have been detected,and the false alarm rate is no longer a factor. So, for identification,the limit to the number of source signatures is less severe, restrictedonly by computational costs. FIG. 4 is a graph that illustrates sourceidentification efficiencies. The graph 400 includes an x-axisrepresenting thickness and a y-axis representing detection efficiency.The solid line 401 represents the identification efficiency of a sourcesignature without shielding. The identification efficiency has a maximumof 0.5 with a thickness of 0 cm and declines to 0.0 with a thickness of5 cm, which means that the source shielded by 5 cm will not beidentified with the source signature without shielding. Solid lines 402and 403 show the identification efficiencies for source signatures of 5cm and 7 cm, respectively. The dashed line 404 connects the detectionefficiencies for those thicknesses with a source signature. The areas405 and 406 represents gaps between the maximum identificationefficiencies and the actual identification efficiencies. Such gaps mayresult in a source being misidentified. The areas of the gaps may bereduced by adding source definition for more shielding thicknesses. Forexample, the gap 405 will be reduced by adding a source definition witha thickness of 3 cm.

The generate metric components includes a generate metric component foreach identification algorithm that is used by the radiation sourceidentifier. In some embodiments, only one identification algorithm isused such as one similar to the OSP matched filter but differs in thenormalization term to account for the differences between detectionefficiency and identification efficiency. The train metric componentsmay be similar to the metric trainer component of the radiation sourcedetector.

The select source metrics component inputs the metrics, identifies themetric that is best for each source, and outputs those metrics as sourcemetrics. For example, the metrics for ¹³⁷Cs without shielding and withshieldings of 1 cm and 2 cm may be 1.0, 2.0, and 0.5, respectively. Insuch a case, the select source metrics component would select 2.0 as thesource metric for ¹³⁷Cs.

The generate null-hypothesis metrics component inputs the measurements,background statistics, and the source metrics and generatesnull-hypothesis metrics representing a statistically worst-case scenariofor the background. The generate null-hypothesis metrics componentcalculates the variance of each channel of a measurement and of theexpected background. The generate null-hypothesis metrics component alsocalculates the variance of each metric and the covariances of the sourcemetrics. The generate null-hypothesis metrics component calculates ascore for the null-hypothesis based on the variances and the covariancesof the source metrics and outputs the variance of the null hypothesis(e.g., average of the variances of the metrics) and the score for thenull hypothesis

The generate source scores component inputs target metrics (i.e., sourcemetrics and the null-hypothesis metrics) and initial probabilities(priors) for each target (i.e., a source and the background). Thegenerate scores component calculates a probability for each target giventhe measurement as the maximum of the probabilities that the metric fora target is greater than the target metric of each other target. Themaximum probability may be based on a multivariate normal distributioncentered at the measurement given the measurement and the variance ofeach target metric. The generate source scores component generates atarget probability (posteriors) for each target, for example, using aBayesian estimator to generate the target probabilities.

The identify radiation source class component inputs targetprobabilities and applies radiation source class rules to generateradiation source class probabilities for each radiation source class.The radiation source class rules store contains the radiation sourceclass rules. Alternatively, the identify radiation source classcomponent may select, for each radiation source class, the maximumradiation source class probability of a target being that radiationsource class. Like the target probabilities, the maximum radiationsource class probabilities may be based on multivariate normaldistribution and the variance for that radiation source class.

Detection Algorithms

The RDA system supports use of various combinations of detectionalgorithms, metric trainers, and aggregation techniques. By supportingsuch combinations, the RDA system can be used in a wide-range ofapplications and can be tailored to those applications.

The RDA system may be used with both projection-based andquadratic-based detection algorithms. A projection-based detectionalgorithm can be decomposed into a set of linear operations that eachoperates on a measurement independently. For example, a detectionalgorithm may generate a metric by calculating a numerator and adenominator by applying a linear transform to the sum of themeasurements over a period of time as represented by the followingequation:

${DM} = \frac{T_{N}\left( {X_{1} + X_{2} + \ldots + X_{k}} \right.}{\sqrt{T_{D}\left( {X_{1} + X_{2} + \ldots + X_{k}} \right)}}$wherein DM represents the metric, T_(N) and T_(D) represent lineartransformation matrices, and X_(i) represents the measurement at time i.Because the numerator and denominator are linear operations, the lineartransform can be distributed as represented by the following equation:

${DM} = \frac{\left( {{T_{N}X_{1}} + {T_{N}X_{2}} + \ldots + {T_{N}X_{k}}} \right)}{\sqrt{\left( {{T_{D}X_{1}} + {T_{D}X_{2}} + \ldots + {T_{D}X_{k}}} \right)}}$Thus, the metric can be independently calculated for each measurement togenerate a numerator and denominator pair represented by the followingequation:U _(i)=(T _(N) X _(i) ,T _(D) X _(i)).which can be aggregated together. The metric can be incrementallyupdated as each measurement is received as represented by the followingequation:

${DM}_{k} = \frac{N_{{DM}_{k - 1}} - {T_{N_{k - n}}X_{k - n}} + {T_{N_{k}}X_{k}}}{\sqrt{D_{{DM}_{k - 1 +}} - {T_{D_{k - n}}X_{k - n}} + {T_{D_{k}}X_{k}}}}$where n represents the number of measurements used to calculate thedecision metric. Because the projection needs only to be made on a newmeasurement, the projection-based detection algorithms are notcomputationally expensive.

With a quadratic-based detection algorithm, the numerator or thedenominator are generated as the product of the measurements through atransformation matrix as represented by the following equation:

${DM} = \frac{\left( {X_{1} + X_{2} + \ldots + X_{k}} \right)^{t}{A\left( {X_{1} + X_{2} + \ldots + X_{k}} \right)}}{\sqrt{T_{D}\left( {X_{1} + X_{2} + \ldots + X_{k}} \right)}}$With a quadratic-based detection algorithm, it is not possible toseparate the interactions the first measurement with the lastmeasurement in a window as represented by the following equation:

${\left( {X_{1} + X_{2} + \ldots + X_{k}} \right)^{t}{A\left( {X_{1} + X_{2} + \ldots + X_{k}} \right)}} = {{\sum\limits_{i = 1}^{k}{\sum\limits_{j = 1}^{k}{X_{i}{TX}_{j}}}} \neq {\sum\limits_{j = 1}^{k}{X_{i}{TX}_{i}}}}$As a result, quadratic-based detection algorithms are morecomputationally expensive than projection-based detection algorithms.The majority of anomaly detection algorithm are quadratic-based.Orthonormal Subspace Projection (“OSP”) Matched Filter

A metric trainer for an OSP matched filter constructs an optimalmultiplicative spectral filter that best eliminates the background. Themetric trainer uses an estimated background basis function B that is anorthonormal subspace projection. This projection is maximum towards asource signature S with a weighting matrix W based on the estimatedbackground. The metric generated by the OSP matched filter has aGaussian distribution and can be normalized to have zero mean and unitvariance. A metric for an OSP matched filter may be represented by thefollowing equation:

${{DM}(X)} = {\frac{S^{t}{W\left( {I - {{B\left( {B^{t}{WB}} \right)}^{- 1}B^{t}W}} \right)}X}{k\sqrt{{X}_{1}}} = \frac{TX}{\sqrt{{X}_{1}}}}$The metric trainer for an OSP matched filter computes the optimumprojection vector T or coefficients (referred to as a transform) for anestimated background, source signature, and basis function. (The symbolt as a superscript represents transpose.) A matched filter is defined byan individual source signature.

The metric trainer inputs a source signature S_(i), an estimatedbackground B, and a background basis vector. The estimated backgroundmay be computed using an exponential smoothing function. The weightingmatrix may be computed by partitioning the previous backgroundmeasurements B. The metric trainer may calculate the optimum projectionvector T as follows:

-   -   1) Compute the inverse of the diagonal of the weighting matrix W        from the estimated background B where each energy bin variance        is the one over the square root of the expected background plus        a scalar    -   2) Compute the weighted background: B_(W)=(B^(t)WB)⁻¹B^(t)W    -   3) Weight the source signature by weighting matrix S^(t)W    -   4) Compute the source signature weighted projection: S^(t)WB    -   5) Compute the background weighted projection: S^(t)WB(B_(W))    -   6) Set the optimum projection vector T to the difference between        the source signature and background weighted projections.    -   7) Set a variance scalar V to the sum of the squares of the        optimum projection vector T times the estimated background:        V=Σ_(i)t_(i) ² b _(i).    -   8) Divide the terms of the optimum projection vector T by the        square root of the expected variance so that the expected        variance is one: T/√{square root over (|X|₁)}.

The performance characteristics of an OSP matched filter may be modeledwith the following equation:

$U = \frac{\epsilon\; S_{Mc}}{\sqrt{S_{Mc} + S_{Mc}}}$where U is the performance characteristic, ϵ is the detection efficiencyassociated with a source signature, S_(Mc) is the total number of countsfrom the source in a measurement, and S_(Mc) is the total number ofbackground counts in the measurement.Hybrid Matched Filter

A hybrid matched filter combines both the properties of matched filtersand the properties of gross count type algorithms. The combination mayallow for more effective detection when the systematic noise and thestatistical noise are similar to each other for the timescale of themaximum integration. Such similarity between systematic noise andstatistical noise is often found in small detectors (e.g., pagers).

A hybrid matched filter does not remove all of the shapes associatedwith the background, but instead removes the portions that do notcorrespond to the source signature. As a portion of the background isbeing retained, a hybrid matched filter estimates and removes the bias.The metric trainer for a hybrid matched filter generates a transform Tas represented by the following equations:{circumflex over (B)}=(I−μS(S ^(t) S)⁻¹ S)T=kS ^(t) W(I−{circumflex over (B)}({circumflex over (B)} ^(t)W{circumflex over (B)})⁻¹ {circumflex over (B)} ^(t) W)where B is the average background per recent unit time, {circumflex over(B)} is the background without the source, and k and μ are variablesadapted to produce a metric with a unit variance and a normaldistribution for the maximum aggregation time specified. A metric for ahybrid match filter may be calculated as represented by the followingequation:

$\mu = {T\;\overset{\_}{B}}$${DM} = \frac{{TX} - {{\mu\Delta}\; t}}{\sqrt{{{\overset{\_}{B}\;\Delta\; t}}_{1}}}$where Δt represents change in time period. A hybrid matched filter maynot be well-suited for long time aggregations as the systematicvariability removal is limited.

The performance characteristics of a matched filter may be modeled withthe following equation:

$D = {\frac{\epsilon\; S_{Mc}}{\sqrt{B_{Mc}}} + \sigma^{2}}$where D is the performance characteristic, ϵ is the detection efficiencyfor the source signature, S_(Mc) is the number of source counts in ameasurement, B_(Mc) is the number of background counts in a measurement,and σ² is a position variable drift term that depends on how well thecurrent background matches the previous background used to estimate. Ahybrid matched filter may perform better than a standard gross countmetric when properly tuned because the position dependent drift isgenerally smaller than that of the gross count metric.Bi-Dimensional (“BD”) Matched Filter

A BD matched filter helps address a problem that occurs when abackground rate is used by a detection algorithm. When the backgroundrate increases suddenly, a detection algorithm may underestimate thebackground. As a result, the mean of the metric will increase becausethis underestimated background is subtracted from the measurement. Also,the variance of the metric will increase because more Poisson noise ispresent when the background increases. Thus, measurements with increasedbackground are likely to result in a metric that is above the thresholdresulting in a false positive.

A BD matched filter, like a hybrid matched filter, allows the intensity(i.e., number of counts) of the measurement over the background tocontribute to the metric along with the change in measurement shape. ABD matched filter introduces nonlinear elements to limit the effects ofthis contribution. A BD matched filter balances these nonlinear elementsso that an increase in the background will decrease the metric resultingin the chance of a false positive being constant regardless of theconditions.

A BD matched filter algorithm does not use an oblique OSP. Rather, a BDmatched filter algorithm computes the total counts in a source signatureand each of the background basis vectors that represent the background.The decomposition will use both the measurement and the expectedbackground measurement. A BD matched filter is bi-dimensional in thesense that it produces both an estimated source and an estimatedbackground for a measurement.

Because the estimated background of a measurement is based both on themeasurement and the estimated background, the estimated background willbe less than the actual background. Thus, the BD match filter algorithmcalculates a penalty by applying a penalty function based on thedifference between the estimated and expected background. The penaltytends to balance the increase in mean and variance in the metric. If,however, the estimated background is less than the expected background,the metric is less likely to result in a false positive and thus nopenalty is required. If a linear penalty function is used, the noise inthe estimated background will be amplified whenever the estimatedbackground is low. A nonlinear penalty function allows a constant falsepositive rate with an estimated background. Further, unlike the OSPmatched filter, the estimated variance is calculated using the expectedbackground. Thus, the metric grows as

$\frac{S}{\sqrt{B}}$rather than

$\frac{S}{\sqrt{S + B}}.$As a result, a BD matched filter has a greater detection per unit countand thus improved sensitivity.

The BD match filter can be based on the partitioned matrix problem asrepresented by the following equation.

${\begin{bmatrix}S & B \\0 & {\lambda\;\overset{\_}{1}}\end{bmatrix}\begin{bmatrix}s \\\overset{\_}{b}\end{bmatrix}} = \begin{bmatrix}X \\{\lambda\; e_{b}}\end{bmatrix}$where S represents the source signature, 1 represents a row vector ofones equal to the number of background components, s represents theestimated counts in the source, b represents a vector of backgroundintensities in each component, B is a matrix of background basisvectors, X is measurement, e_(b) is the estimated total count in thebackground, and λ is a tuning parameter based on reliability of theestimated background total counts.

This partitioned matrix problem can be computationally expensive tosolve directly because the vector projection of each of the source andbackground components would need to be calculated for each timestep. Thepartitioned matrix problem can be solved by decomposing it into aproblem with the solution represented by the following equation:

$\begin{bmatrix}s \\b\end{bmatrix} = {X + e_{b}}$Because it is not necessary to solve for the individual backgroundcomponents, the BD matched filter estimates only a single totalbackground term. As a result, the BD matched filter can use the samegeneral framework used by other matched filters.

After the estimated source count and the estimate background count aregenerated, a metric for the DB matched filter may be calculated asrepresented by the following equation:

${DM} = \frac{c - {\kappa\mspace{11mu}{\max\left( {{b_{t} - e_{b}},0} \right)}}}{\sqrt{e_{b}}}$

Like the other matched filter algorithms, the numerator and denominatorterms can be summed to integrate over multiple time segments. Thepenalty may be computed such that total integrated distribution isconstant for different conditions of background intensity and backgroundestimate terms. As the distribution function is not linear nor fixed asa function of threshold, a slope may be selected such that thedistribution integrate is never greater than expected. The slight overpenalty that it produces may reduce our overall sensitivity, but thissensitivity loss may not be much less than the sensitivity gained fromthe use of an estimated background in the denominator term.

Likelihood Ratio Test (“LRT”) Matched Filter

An LRT matched filter may employ the same metric evaluator as a hybridmatched filter. The LRT matched filter uses Poisson probabilitystatistics for measurements to detect a source signature when thebackground shape is known. The LRT matched filter removes only onesource signature at a time. An LRT matched filter has excellentsensitivity in static detection scenarios and may be suited for smalldetectors for which the statistical noise is a primary factor. The LRTmatched filter may not be suited for large mobile detectors.

The LRT matched filter constructs two hypotheses as represented by thefollowing equations.H ₀ =B _(s) B _(r) ΔtH ₁ =B _(s) B _(r) Δt+S _(s) k√{square root over (B _(r) Δt)}where H₀ and H₁ are the hypotheses, B_(s) represents a background shape,S_(s) represents a source signature shape, B_(r) represents a backgroundrate estimate, and Δt represent change in time period. Each shape is aspectrum with a total count of 1. The LRT matched filter algorithmcalculates the likelihood of each measurement given Poisson statisticsas represented by the following equation:

${P\left( {X❘H} \right)} = \frac{\prod_{i}{{\exp\left( {- h_{i}} \right)}h_{i}^{x_{i}}}}{x_{i}!}$where h_(i) represents the i-th element of the hypothesis. The ratio ofthe two hypotheses results in the factorial terms canceling. The LRTmatched filter also takes the log to convert the exponentials into sums.The resulting LRT may be represented by the following equation:

${{LRT}(X)} = {{{{\log\left( {1 + {\frac{k}{\sqrt{B_{r}\Delta\; t}}\frac{S_{s}}{B_{s}}}} \right)}^{t}X} - \frac{k}{\sqrt{B_{r}\Delta\; t}}} = {{T^{t}X} + M}}$

The resulting LRT is based on a two-sided test, while other matchedfilters may be based on one hypothesis tests with normal statistics.Thus, the resulting LRT may be transformed by subtracting the mean withexpected background and dividing by the expected noise for the estimatedbackground. The result represents a one hypothesis test for background.The expected background rate is needed to generate the metric. As aresult, the metric may be represented by the following equation:

${DM} = \frac{\left( {{T^{t}X} + {\kappa\; B_{r}\Delta\; t}} \right)}{\sqrt{{vB}_{r}\Delta\; t}}$where DM represents the metric and K represents a bias term (or penalty)for any difference in the estimated total count in the background and anexpected background total count in the background. Bias term is anadjustment that may be made observationally based on the trust abackground estimate. If a simulation with a known statisticallydistribution (truth) is run and random draws are used to form anestimate, there can be a bias term. If this bias term is in the negativedirection, a source will be detected more often than expected. Thus, abias term is added to account for the differences between truth (theactual expected background) and the estimate background. An LRT matchedfilter is only optimal for a target time window and single intensity.These target values are supplied to the metric trainer.

The LRT matched filter follow a behavior model given as

$D = {\frac{\epsilon\; S_{M\; c}}{\sqrt{B_{M\; c}}} + \sigma^{2}}$where ϵ represents the detection efficiency for the source S, S_(Mc)represents the number of source counts in a measurement, B_(Mc)represents the number of background counts in a measurement, and σ²represents a position variable drift term that depends on how well thecurrent expected background matches the previous expected background.The hybrid matched filter may be a better performer than the standardgross count metric when properly tuned because the position dependentdrift is always smaller than that of the gross count metric.Targeted NSCRAD Algorithm

NSCRAD Algorithm

The Nuisance-Rejection Spectral Comparison Ratio Anomaly Detection(“NSCRAD”) algorithms were developed by the Pacific Northwest NationalLaboratory. The NSCRAD algorithm first converts a measurement into a setof regions of interest and applies a transform to remove the expectedbackground vector. Because the regions of interest may be overlapping,the NSCRAD algorithm factors in a correlation matrix estimated frombackground samples.

Given a region of interest feature vector X∈

^(n) and a region of interest background vector B=[b_(i)], the NSCRADtransform α is an

^(n-1×n) may be represented by the following equation:

$\alpha = \begin{bmatrix}1 & {- \frac{b_{1}}{b_{2}}} & 0 & \ldots & 0 \\1 & 0 & {- \frac{b_{1}}{b_{3}}} & \ddots & 0 \\\vdots & \vdots & \ddots & \ddots & \vdots \\1 & 0 & 0 & \ldots & {- \frac{b_{1}}{b_{n}}}\end{bmatrix}$Because the regions of interest may be overlapping, an estimate of thecovariance Σ is employed.

In addition to the removal of the background components, the NSCRADalgorithm may employ a subspace projection matrix γ. The NSCRADalgorithm computes from a matrix of background sources N∈

^(n×m). The NSCRAD algorithm calculates a subspace projection transformas represented by the following equation:γ(N)=(I−N(N ^(t) N)⁻¹ N ^(t))

Because the feature vectors are projected and are not independent, theNSCRAD algorithm may use an oblique subspace projection. The transformedcovariance matrix may be represented by the following equation:β=(αΣα^(t))⁻¹αwhere Σ represents covariance. The NSCRAD algorithm adjusts the subspaceprojection to remove the background as represented by the followingequation:γ(N;α,β)=(I−αN(N ^(t)β^(t) αN)⁻¹ N ^(t)β^(t))

Many different algorithms may be developed from these transforms. TheNSCRAD algorithm may be represented by the following equation:DM=X ^(t)(β^(t)γα)XThis is a classical quadratic form where Q=β^(t)γα. This metric willhave χ² statistics with the number of degrees of freedom given by thenumber of energy features minus one for the removal of background andminus the number of background sources removed. An implementation of theNSCRAD algorithm may be as described as follows:

   Let {circumflex over (B)} be a feature vector with unit counts    Let{circumflex over (Σ)} be a covariance estimate per unit count    Let λbe a forgetting factor  Let a subspace projection to eliminate thebackground be γ(N; α, β) =   I − αN (N^(t) β^(t) αN)⁻¹ N^(t) β^(t)   For each measurement X For each region of interest in regions ofinterest Y of X  Compute α as SCR transform for {circumflex over (B)} Compute β from the covariance estimate {circumflex over (Σ)} and α,with β = (α{circumflex over (Σ)}α^(t))⁻¹ α   ${{Compute}\mspace{14mu}{decision}\mspace{14mu}{metric}\mspace{14mu}{DM}} = \frac{Y^{t}\beta^{t}{\gamma\left( {{N;\alpha},\beta} \right)}\alpha\; Y}{{X}_{1}}$  If (m < threshold) revise the background estimates    ${{Update}\mspace{14mu}\hat{\Sigma}} = {{\lambda\;\hat{\Sigma}} + {\frac{\left( {1 - \lambda} \right)}{{X}_{1}}\left( {Y - {\hat{B}{X}_{1}}} \right)\left( {Y - {\hat{B}{X}_{1}}} \right)^{t}}}$   ${{Update}\mspace{14mu}\hat{B}} = {{\lambda\;\hat{B}} + {\frac{\left( {1 - \lambda} \right)}{{X}_{1}}Y}}$

Since the NSCRAD algorithm has a quadratic form, the aggregation isperformed on a spectral comparison ratio (“SCR”) vector and theprojected SCR vector, rather than simply the numerator and denominatorin other algorithms. As a result, the amount of memory that is neededincreases when aggregating measurements spatially. Thus, an NSCRADalgorithm might be appropriate when using temporal aggregation but mightnot be appropriate when using spatial aggregation.

The NSCRAD algorithm has inherently χ² statistics. As such, it may notwork with the metric aggregation as described below. The NSCRADalgorithm only removes a single dynamic degree of freedom as well as aspecified number of fixed background dimensions. This approach reducesthe complexity of the background estimator but may result in a reductionin the systematic variability attenuation. As such, the maximumaggregation time is limited for large detectors. The NSCRAD algorithmmay suffer from poor statistical distributions when the total counts arelow. This tendency requires increasing the threshold about the levelexpected by χ² statistics in some cases.

NSCRAD Matched Filter

An NCRAD matched filter, unlike the NSCRAD algorithm, has statisticswith unit variance and normal distributions. As a result, the tendencyto have outliers is reduced and there is an increase in the systematicvariance rejection leading the wider range of operation. A down side ofthis approach is the regions of interest that optimize the performanceare different for each target source. Thus, every detection metricevaluator needs to be paired with a corresponding metric trainer. Animplementation of the NSCRAD matched filter is the same as that for theNSCRAD algorithm except that for each region of interest the matchedfilter coefficients are calculated as represented by the followingequation:G=S ^(t)β^(t)γ(N;α,β)αand the decision metric is represented by the following equation:

$m = {\frac{GY}{\sqrt{{X}_{1}}}.}$

Unlike the NSCRAD algorithm, the NSCRAD matched filter generates ametric for one source signature. To cover a range of sources with afilter, a description of the source signatures covering the sources tobe detected is needed.

Metric Aggregation

The aggregate metrics component may aggregate metrics spatially ortemporally. Many spectral detection algorithms aggregate in a fixed timewindow. A spatial aggregation technique generates an aggregate metricfrom measurements collected at the same location but at different times(e.g., a detector makes multiple sweeps over an area). A temporalaggregation technique generates an aggregate metric for each timeinterval based on the immediate prior time intervals. The aggregatemetrics component may employ a variety of aggregation techniques togenerate the aggregated metrics. When the RDA system employs multipleaggregation techniques, the aggregation metric component generatesaggregated metrics for each aggregation technique. The evaluate metricscomponent may use the maximum of the aggregated metrics to detectwhether a source is present.

A temporal aggregation technique employs a rolling window ofmeasurements when generating the aggregated metrics. The aggregationcomponent generates an aggregated metric for multiple rolling windowsthat each contains a different number of measurements.

A spatial aggregation technique may apply a weighted back projectionmethod assuming a simple 1/R² kernel to generate the aggregated metric.

Metric Standardization

The generate metric component may employ a metric standardizationtechnique to correct the statistical distribution of detectionalgorithms that produce standard normal distribution on their individualmetrics. The metric standardization technique maintains a runningestimate of the recent mean and variance of each of the metrics. Fromthis running estimate, the metric standardization technique computes thedeviation in mean from zero (bias) and the excess variance. The metricstandardization technique then corrects the detection metric to removethis bias and reduce the variance.

The metric standardization technique may employ a bias forgetting factorand variance forgetting factor. The metric standardization techniqueemploys these factors in a first order infinite impulse response filterto maintain the bias and variance estimates. The smaller the number theslower the algorithm will learn about changes in conditions. Forexample, the forgetting factors may be on the order of 0.01 which meansthat the metric standardization technique learns changes on the order of100 measurements. These forgetting factors may be smaller when thebackground is very stationary and larger when the background is highlyvarying. The bias and variance forgetting factors may have the same ordifferent values. The metric standardization techniques may excludemetrics that clearly contains a source when calculating these bias andvariance estimate. For example, metrics that exceed a threshold may beexcluded.

Measurement Calibration

The RDA system calibrates measurements to account for the drift in asensor of a detector during its collection of photons. The drift resultsin photons of one energy level to be identified as different energylevels and may scale the spectrum to be wider or narrower. To accountfor this drift, the calibrate measurements component allocates countsfrom the measurement bins to different bins referred to changing thebinning structure of the measurement or rebinning. To identify thecharacteristics of the drift, the radiation source detector mayperiodically collect from the detector calibration measurements of aknown source and/or measurements from background. A calibratemeasurements component identifies the calibration that is needed bycomparing the calibration measurements to the expected measurements ofthe known source to determine the drift. For example, if the sourcesignature of the known source and the calibration measurements havecorresponding peaks in different bins, the count of the measurementneeds to be re-allocated based on the difference in the bins. Forexample, in a simple case, the measurement may have the exact shape asthe source signature except that because of the drift the measurement isshifted by one bin to a lower energy level. The calibration componentmay also correct for nonlinearities in the detector response includingthose intrinsic to the photon absorber and those from the amplifier.These nonlinearities are generally captured in characterizationsmeasurements made prior to deployment and provided in the form ofpolynomial or spline coefficients, or energy/channel pairs.

In some embodiments, strong measurements of sources with well isolatedfeatures can be used to monitor, and in some cases provide additionalcorrections to the nonlinearities. To accomplish this, each sourcesignature is augmented with a lookup table with the effective energy foreach feature (e.g., gamma-ray emission line or Compton edge) in thesource signature that is useful for calibration. The effective energy isthe energy that would be found for a perfectly measured source signaturethat takes into account shifts from the true energy due to the impact ofshielding and scattering on the feature position extraction procedure(e.g., centroiding or line fitting). In addition to the effectiveenergy, the expected range of effective energies (e.g., standarddeviation) is listed in the table along with the range of ratio of thecounts in the peak to those in the entire spectrum. The effectiveenergy, range of effective energy, and peak-to-total count ratio can allbe a function of the signal-to-noise ratio of the measurement and thetotal number of counts in the measurement. These dependencies areincluded in the lookup table of a source signature. Settings for thefeature extraction procedures (e.g. regions of interest) are alsoincluded in the lookup table. Some source signatures, such as those withhigh amounts of shielding or for nuclides without well-defined features,will be unsuitable for calibration monitoring and correction and willnot have any effective energies and related values included.

When a source is detected with a sufficiently high decision metric andsource identification probability, the effective energy for each featureis calculated and compared with those in the lookup table of the sourcesignature. If the extracted energy differs from the effective energylisted in the lookup table significantly more than the expected rangefor the measurement conditions encountered, then a flag can be set toindicate a calibration problem (calibration monitor), and/or theinformation can be used to update the energy scale (calibrationcorrection). The measured peak-to-total count ratio is also comparedwith an appropriate range in the lookup table to validate that thecorrect feature and source signature has been found regardless of energyscale. More than one measurement of the calibration error is required totrigger a correction. A corrected energy scale can then be generatedusing a constrained spline that maintains the overall shape of thenonlinearities from the more detailed and controlled pre-deploymentcalibration measurements for a specific detector, or for the detectortype in general, but also adjusts to more closely match the measurementsat the energies available.

To account for changes in the energy scale due to drift andnonlinearities, the calibrate measurements component re-allocates thecounts of each bin of the measurement to the next higher energy level.As another example, the drift of the energy range may vary across theenergy range such as counts in the lower energy bins may compressed intofewer bins while the counts in the higher energy bins may be expanded tomore bins. As another example, when the counts of a bin of a measurementneed to be allocated to multiple bins, there may be a correlation in thestatistics of the data. This correlation may cause statistical analysistechniques to produce biases that can affect the detection metrics. Toprevent such statistical biases, the calibrate measurements componentemploys a statistical rebinning method. For example, the calibratemeasurements component may randomly select a binomial variable of numberof counts in a bin of the measurement that need to be re-allocated toanother bin. By introducing this randomness, the Poisson statistics ofthe rebinning is preserved. Nonlinearities correction may take intoaccount environmental factors such as temperature, or operationalfactors such as count rate. Thus, the coefficients may be a function ofthese factors.

FIG. 5 illustrates calibration of measurements based on drift in adetector. The measurement 510 includes an expanded bin 511 with expandededges 512 and 513. The calibrated measurement 520 include bins 521-523with edges 524-527 specified by the binning structure. The calibratedmeasurement represents a compression in the scale of the measurement.Bin 511 overlaps bins 521-523. To distribute the counts of bin 511 tobins 521-523, the calibrate measurements component allocates a fractionof the count of bin 511 to bin 521. The fraction is based on ratio ofthe energy range 528 to the energy range of bin 511. Similarly, thefraction of counts corresponding to energy range 529 are allocated tobin 523. The remaining count are allocated to bin 522. Rather than usingthe exact fraction number of counts, the calibrate measurementscomponent may select the number of counts based on a binomialdistribution for that fraction number of counts.

Dynamic Background Estimation

The RDA system provides for the dynamic estimation of the background inmeasurements. When a detector is moving, the measured background canvary significantly (e.g., by a factor of five). A gross count algorithmsuch as a gross counts k-sigma algorithm will be dominated by thesevariations and have little sensitivity to weak sources. Spectraldetection algorithms detect sources by looking for changes in spectralsignature rather than in overall intensity. Thus, spectral detectionalgorithms are trained with a set of representative backgrounds for theregion to be measured. Such representative backgrounds are typicallydeveloped based on collecting many hours of background data that is usedas a fixed background training set.

The generating of representative backgrounds may be difficult in somecases. For example, when a detector is to be deployed to a new area, itmay not be practicable to collect background data. Also, the response ofa detector may change over time. These changes can include gain drifts,changes in non-linearity response, and changes in energy resolution andefficiency. The calibrate measurements component will correct gaindrifts but is limited in correcting other changes in the detectorresponse. As a result, a representative background generated frombackground data collected on one day may not represent the backgrounddetected on another day. To make detection sensitivity and falsepositive rates more reliable, robust, and predictable, the RDA systemmay use dynamic background estimation that is trained “on the fly”during normal operations.

Since the RDA system receives measurements from detectors that are notonly measuring background but also measuring sources, the estimatebackground components excludes the contributions of the source. Thesesources can include intentional sources, unknown sources found as partof a source search, and chance encounters with sources that are commonlyfound in the environment (e.g., medical). To improve the estimatedbackground, the estimate background component recognizes when ameasurement represents a chance source and excludes those measurementswhen generating the estimated background. The RDA system employs a vetocomponent that analyzes the metrics to decide which measurements toexclude. It is possible that a measurement of a source may not beexcluded. For example, if the source is present when the detector startscollecting measurements or if the radiation from the source graduallyincreases, the source may not be detected and thus not be excluded. As aresult, a small amount of the source signature could be incorporatedinto the background, desensitizing the background estimate to thissource. In such a case, a measurement of the source based on a slightlylarger intensity would not be recognized as a source and moreover wouldnot be excluded.

To prevent such sources from not being detected and thus not excluded,the RDA system employs a sensitivity tester. Periodically, thesensitivity tester checks the sensitivity to a large number of sourcesby injecting the source with varying intensities into the estimatedbackground to generate test measurements. The sensitivity tester thenruns the detection algorithms with the estimated background generated bya background estimation component against the test measurements todetermine the minimum intensity of a source that is required fordetection. The sensitivity tester compares this minimum intensityagainst previous minimum intensities to determine whether thesensitivity has been compromised. If so, the estimate backgroundcomponent is restarted to generate a new estimated background.

Rank Average

The rank average algorithm generates a background rate for everymeasurement that is received. The RDA system may use a rank averagealgorithm with gross count detection algorithms when operatingconditions in which the background of a measurement is temporarilydecreased or increased. For example, when an object (e.g., truck) passesthe detector, the background rate may decrease, and when the detectorpasses a structure (e.g., building) that contains a source, thebackground rate may increase.

To account for the temporary decrease or increase in background, therank average algorithm, sorts the total counts of each measurementcollected over a certain time period. The rank average algorithm thengenerates an average of the top (or bottom) counts. This average is abiased estimate of the background. Using standard statisticalassumptions, the rank average algorithm calculates the bias factor. Therank average algorithm then corrects the biased estimate based on thebias factor. For example, the rank average algorithm may calculate anexpected offset for a Gaussian distribution in the average total countsand subtract that offset from the estimated background.

The rank average algorithm may be particularly useful when a detector isstationary, when the background is being suppressed by nearby objects,and when an estimated background is needed in the absence of thoseobjects.

Progressive Projection

The RDA system employs a progressive projection algorithm estimating aset of background vectors that span through linear combinations of allpossible background shapes. This spanning set is referred to as abasis—although the background vectors are not orthogonal. Theprogressive projection algorithm queues background measurements (i.e., ameasurement with no source detected) until either there is a change inthe spectral shape of the background measurement or a certain minimumtime has elapsed. The progressive projection algorithm then compares thebackground measurements with the changed spectral shape to the currentbasis vectors to determine which basis vector it best represents. Ifbackground measurement is found to not be strongly associated ordisassociated with any of the existing basis vectors, that backgroundmeasurement represents nothing of novel about the background.Conversely, if it is found to be strongly associated with or dissociatedwith a basis vector, the progression projection algorithm averages thatbackground measurement into the existing basis vector. The progressionprojection algorithm then tries to maximize the eigenvalues associatedwith a correlation matrix formed by the basis vectors such that thebasis vectors have maximum diversity. The progressive projectionalgorithm employs a forgetting function so that if the backgroundmeasurements are the same for a long duration, the progressiveprojection algorithm does not update the basis vectors so that they donot become collinear.

The progressive projection algorithm may initialize the basis vectorsfrom the background measurements collected after the algorithm isstarted or cleared. If detector is exposed to a source duringinitialization, that source may be trained into the basis vectors andthus become insensitive until a sensitivity tester is activated. Theprogressive projection may have three starting methods. A hot startmethod uses a set of predefined basis vectors specified in configurationdata. A warm start method generates an average of the backgroundmeasurements over a specified period and then mixes the average withpredefined background measurements from the configuration data (or aprevious run). A cold start method takes the average over the specifiedperiod and then injects known background measurements.

Each start method has different advantages and disadvantages. The coldstart method is robust in the sense that it will operate even if thereis a source present collocated with the detector so long as that sourceis not removed. But this robustness comes at the cost that the methodmay take a prolonged period before it becomes fully trained and thusreaches full sensitivity. If the known background measurements of thecold start method do not accurately reflect the variability in thebackground measurements, then the RDA system may not correctly detectsources and need to be restarted. The warm start method uses previousbackground measurements and thus becomes trained quickly. However, ifthe background measurements are very different from the previousbackground measurements (e.g., a source is present in the backgroundmeasurements), the progressive projection algorithm will becomeinsensitive to that source and need to be restarted. A hot start methodcan immediately generate the basis vectors. However, if the backgroundmeasurements are not represented by the predefined basis vectors, theRDA system may generate false positives, that is, a backgroundmeasurement is indicated as having a source present. As a result, theprogressive projection algorithm will not update the basis vectors andthe false positives will continue and need to be restarted using thecold start method or the warm start methods.

Classification

In some embodiments, the generate metrics component employs anidentification algorithm that is similar to the OSP match filterdetection algorithm but differs in the normalization term. The generatedmetrics component calculates the expected variance Σ_(B) in each channelusing the expected background calculated prior to the measurement. Ifmixtures of source signatures of a source are considered, the generatemetrics component calculates the mixture of the source signatures foreach source that optimally solves Σ_(B)X=Σ_(B)Ax to create a mixedsource signature S=Ax. If mixtures are not considered, the generatemetrics component uses each source signature S_(i).

For each source signature (mixed or not mixed), the generate metricscomponent calculates a transform as represented by the followingequation:

$Q_{i} = \frac{S_{i}^{t}{\Sigma_{B}^{- 1}\left( {I - {{B\left( {B^{t}\Sigma_{B}^{- 1}B} \right)}^{- 1}B^{t}\Sigma_{B}^{- 1}}} \right)}}{\sqrt{S_{i}^{t}{\Sigma_{B}^{- 1}\left( {I - {{B\left( {B^{t}\Sigma_{B}^{- 1}B} \right)}^{- 1}B^{t}\Sigma_{B}^{- 1}}} \right)}S_{i}}}$where Q_(i) represents the transform. The generate metrics componentthan applies this transform to the measurement as represented by thefollowing equation:k _(i) =Q _(i) ·Xwhere k_(i) represents the score.

The generate null-hypothesis component calculates the variance Σ_(Y) ofthe measurement using the expected background. The generate thenull-hypothesis component generates the variance of each metric and thecovariances of the metrics using the following equations.σ_(i) ² =Q _(i)Σ_(X) Q _(i) ^(t)c _(ij) =Q _(i)Σ_(X) Q _(j) ^(t)where σ_(i) ² represents the variance of S_(i) and c_(ij) represents thecovariance of S_(i) and S_(j). The generate null-hypothesis componentselects that largest of the variance to for the null hypothesis. Thegenerate null-hypothesis component then calculates the metric for thenull hypothesis based on the maximum of a random walk around zero with afixed number of draws as represented by the following equation:

$k_{null} = {\sigma_{m}\left( {\sqrt{\log\;\frac{n^{2}}{2\pi\;{\log\left( \frac{n^{2}}{2\pi} \right)}}} \cdot \left( {1 + \frac{\gamma}{\log\; n}} \right)} \right)}$where k_(null) represents the metric, n represents the number ofhypothesis times one over the desired probability of incorrectlyidentifying that a source is present, and γ represents theEuler-Mascherioni constant. (See, David and Nagaraja, “OrderStatistics,” John Wiley & Sons, § 10.5, 2004.) The generatenull-hypothesis component calculates the variance of the null hypothesisas the average of the variances of the metrics.Computing System

The computing systems (e.g., network nodes or collections of networknodes) on which the RDA system and the other described systems may beimplemented may include a central processing unit, input devices, outputdevices (e.g., display devices and speakers), storage devices (e.g.,memory and disk drives), network interfaces, graphics processing units,cellular or other radio link interfaces, global positioning systemdevices, inertial navigation, and so on. The input devices may includekeyboards, pointing devices, touch screens, gesture recognition devices(e.g., for air gestures), head and eye tracking devices, microphones forvoice recognition, and so on. The computing systems may includehigh-performance computing systems, cloud-based servers, desktopcomputers, laptops, tablets, e-readers, personal digital assistants,smartphones, gaming devices, servers, and so on. For example, thesimulations and training may be performed using a high-performancecomputing system, and the classifications may be performed by a mobiledevice that is part of the network node. The computing systems mayaccess computer-readable media that include computer-readable storagemedia and data transmission media. The computer-readable storage mediaare tangible storage means that do not include a transitory, propagatingsignal. Examples of computer-readable storage media include memory suchas primary memory, cache memory, and secondary memory (e.g., DVD) andother storage. The computer-readable storage media may have recorded onthem or may be encoded with computer-executable instructions or logicthat implements the RDA system and the other described systems. The datatransmission media are used for transmitting data via transitory,propagating signals or carrier waves (e.g., electromagnetism) via awired or wireless connection. The computing systems may include a securecryptoprocessor as part of a central processing unit for generating andsecurely storing keys and for encrypting and decrypting data using thekeys.

The RDA system and the other described systems may be described in thegeneral context of computer-executable instructions, such as programmodules and components, executed by one or more computers, processors,or other devices. Generally, program modules or components includeroutines, programs, objects, data structures, and so on that performtasks or implement data types of the RDA system and the other describedsystems. Typically, the functionality of the program modules may becombined or distributed as desired in various examples. Aspects of theRDA system and the other described systems may be implemented inhardware using, for example, an application-specific integrated circuit(“ASIC”) or field programmable gate array (“FPGA”).

Flow Diagrams

FIG. 6 is a flow diagram that illustrates high-level processing of anOSP match filter component of the RDA system in some embodiments. Thecomponent 600 is invoked passing an indication of measurement X_(t) fortime t and a source definition Ŝ. In block 601, the component calculatesa transform projection T that is complimentary to the background B. Inblock 602, the component calculates a partial projection of X_(t) withthe background removed onto the source definition Ŝ. In block 603, thecomponent estimates the partial noise for n_(t) time t. In block 604,the component updates the projection for a moving window to add in thepartial projection for time t and subtract the partial projection fortime t−Δ. In block 605, the component updates the noise for the movingwindow to add in the partial noise for time t and subtract the partialnoise for time t−Δ. In block 606, the component calculates the detectionmetric as the projection divided by the noise. The component thencompletes.

FIG. 7 is a flow diagram that illustrates the process of a calibratecomponent of the RDA system in some embodiments. The component 700 isinvoked to revise the existing bins based on the drift of a sensor ofthe radiation detector in the detection of the energy of the signals.The component is invoked passing an indication of the edges andmeasurements of the current bins and a scale s. In blocks 701-205, thecomponent initializes the number of overflow bins (#OFbins) andunderflow bins (#UFbins). In block 701, the component sets the number ofoverflow bins to 0. In decision block 702, if the scale is less than1.0, then the component continues at block 703, else the componentcontinues at block 704. In block 703, the component sets the number ofunderflow bins to the number of bins (#bins) minus the floor of thenumber of bins multiplied by the scale. In block 704, the component setsthe number of underflow bins to 0. In decision block 705, if the scaleequals 1.0, then no rebinning is needed and the component completes,else the component continues at block 706. In block 706, the componentinitializes variables b0 and b1 to track the energy of the bin edges andan index i for the bins. In block 707-713, the component loopsperforming the rebinning. In block 707, the component increments theindex i to the next bin. In decision block 708, if all the bins havealready been indexed, then the component completes, else the componentcontinues at block 709. In block 709, the component set b0 to thecurrent edge and b1 to the next edge. In block 710, the component setsindex i0 and i1 to the flow of index b0 and index b11, respectively. Indecision block 711, if index i0 equals index i1, then the componentcontinues at block 713, else the component continues at block 712. Inblock 712, the component invokes a split bin component to split the binsand loops to block 707 to select the next bin, else the componentcontinues at block 713. In block 713, the component increments the countof the new bin out[i0] by the counts of the measurement X[i] and loopsto block 707 to select the next bins.

FIG. 8 is a flow diagram that illustrates the processing of a split bincomponent of the RDA system in some embodiments. The component 800 isinvoked to split a bin. In block 801, the component sets the variable nto the counts of bin[i] and the variable split to the fraction of thosecounts for allocation to out[i0]. In block 802, the component applies abinomial distribution to select the number of counts x1 to be added toout[i0] based on the variables n and split. In block 803, the componentadds the count x1 to out[i0] and decrements the remaining counts ofvariable n by counts x1. In block 804, the component sets index j toindex i0 plus 1. In decision block 805, if variable j is greater thanthe minimum of variable x1 and the number of bins #bins, then thecomponent completes, else the component increments variable j andcontinues at block 806. In block 806, the component sets the variable x1to a value based on a binomial distribution of the variable n and 1.0divided by the scale s. In block 807, the component decrements thevariable n by the variable x1. In block 808, the component incrementsthe count of out[i1] by the variable x1. In decision block 809, if indexi1 is less than the number of bins, then the component continues atblock 801, else the component continues at block 811. In block 810, thecomponent increments out[i1] by the variable n and loops to block 805.In block 811, the component sets the count of the overflow to variable nand loops to block 805.

FIG. 9 is a flow diagram that illustrates the processing of an estimatebackground based on rank average component of the RDA system in someembodiments. The component 900 is invoked to adjust the estimate of thebackground. In block 901, the component initializes a variable i toindex through the bins. In decision block 901, the component incrementsthe variable i and if greater than the number of bins, the componentcompletes, else the component continues at block 903. In block 903, thecomponent creates a sort of the counts within the moving window for theindexed bin. In block 904, the component calculates the average of thetop counts. In block 905, the component calculates an expected offsetfor the top counts based on the average. In block 906, the componentadjusts the counts for the indexed bins by the offset and loop to block902 to select the next bin.

The following paragraphs describe various embodiments of aspects of theRDA system and other systems. An implementation of the systems mayemploy any combination of the embodiments. The processing describedbelow may be performed by one or more computing systems with processorsthat executes computer-executable instructions stored on acomputer-readable storage medium that implements the system.

In some embodiments, one or more computing systems for identifying asource of radiation is provided. The one or more computing systemcomprise one or more computer-readable storage mediums storingcomputer-executable instructions and one or more processor for executingthe computer-executable instructions stored in the one or morecomputer-readable storage mediums. The instructions implement aradiation source detector that receives measurements of radiation; forone or more sources, generates a detection metric indicating whetherthat source is present in the measurements; and evaluates the detectionmetrics to detect whether a source is present in the measurements. Theinstructions also implement a radiation source identifier that, when thepresence of a source in the measurements is detected, for one or moresources, generates an identification metric indicating whether thatsource is present in the measurements; generates a null-hypothesismetric indicating whether no source is present in the measurements; andevaluates the one or more identification metrics and the null-hypothesismetric to identify the source, if any, that is present in themeasurements. In some embodiments, the radiation source detectorgenerates the detection metric for a source based on a detection sourcesignature for that source and the radiation source detector generatesthe identification metric for a source based on a plurality ofidentification source signatures for that source. In some embodiments,the identification sources signatures for a source represent that sourcewith different shieldings. In some embodiments, the radiation sourceidentifier identifies a radiation source class for an identified source.In some embodiments, the measurements are calibrated to account fordrift in the detector that collected the measurements. In someembodiments, the radiation source identifier generates an aggregatedmetric for each source. In some embodiments, the measurements with in awindow are aggregated and radiation is detected and identified based onaggregated measurements.

In some embodiments, a method performed by a computing system fordetecting presence of a source of radiation from a measurement ofradiation. For one or more sources and for one or more detectionalgorithms for each source, the method generates a metric using thatdetection algorithm wherein the metric indicates similarity between acurrent measurement and a source signature for that source and whereinthat detection algorithm factoring in an estimate of backgroundradiation generated based on prior measurements to the currentmeasurement; and generates an aggregated metric for that source from oneor more metrics for that source. The method analyzes the aggregatedmetric for one or more sources to determine whether a source is likelypresent in the current measurement. The method, upon determining that asource is likely present in the current measurement, indicates that thepresence of a source has been detected. In some embodiments, the methodfurther generates an estimate of background radiation based on priormeasurements excluding prior measurements for which the presence of asource was detected. In some embodiments, estimate of backgroundradiation is a weighted average based on a measurement and a priorestimate of background radiation. In some embodiments, a sourcesignature is associated with an identification of a source and ashielding of the source. In some embodiments, for each detectionalgorithm, the method generates a metric for each source signatureassociated with a source. In some embodiments, a measurement isrepresented by a histogram of energy ranges with a count of photons foreach energy range. In some embodiments, a metric is generated for eachof a plurality of windows measurements, each window including thecurrent measurement and a number of adjacent prior measurement. In someembodiments, a detection algorithm is associated with coefficients andthe method further dynamically adjusts the coefficients based on anestimate of background radiation. In some embodiments, the analyzing ofthe aggregated metrics includes applying a classifier that is trainedusing training data that includes feature vectors with features thatinclude detection algorithm, a window size, and an aggregated metric anda label for each feature vector indicating whether a source is present.In some embodiments, the feature vectors are generated from collectedmeasurements of radiation. In some embodiments, the feature vectors aregenerated from simulated measurements of radiation. In some embodiments,the method further calibrates the current measurement to account fordrift associated with a detector used to collect the measurements.

In some embodiments, a method performed by a computing system isprovided for identifying a source of radiation from a measurement ofradiation. The method receives a current measurement for which presenceof a source has been detected based on detection source signatures. Foreach of a plurality of identification source signatures, the methodgenerates a metric using an identification algorithm. The metricindicates similarity between a current measurement and a sourcesignature for that source. The identification algorithm factors in anestimate of background radiation generated when detecting the presenceof the source. The identification source signatures being morecomprehensive than the detection source signatures. For each source, themethod generates a source metric based on one or more metrics generatedusing a source signature for that source. The method generates anull-hypothesis metric indicating similarity between the currentmeasurement and the estimate of background radiation. The sources andthe estimate of background radiation are targets. The source metrics andthe null-hypothesis metric are target metrics. For each target, themethod generates a target probability representing presence of thattarget in the current measurement. The target probability is based onthe target metric for that target. In some embodiments, the method, foreach of a plurality of radiation source classes, generates a radiationsource class probability that the current measurement represents asource of that radiation source class. The radiation source classprobabilities is generated based on target probabilities. In someembodiments, the generating of a target probability factors in priorprobabilities for each target. In some embodiments, the identificationalgorithm is based on an orthonormal subspace projection matched filteralgorithm.

Orthonormal Subspace Projection Matched Filter Algorithm

In some embodiments, a method performed by one or more computing systemsis provided for generating a metric relating to a source of radiation inmeasurements of radiation. The method accesses a source signature of thesource, an estimated background, and background basis vectors. Themethod generates a projection vector based on the source signature,estimated background, and the background basis vectors. The methodaccesses a measurement. The method generates the metric based on themeasurement, the projection vector, and an expected variance for themeasurement. In some embodiments, the source signature is a histogramrepresenting an energy range divided into energy bins, each energy binhaving a value representing a count of photons emitted by that sourceover a time interval. In some embodiments, the generating of theprojection vector includes generating a weighting matrix from theestimated background wherein a variance of each energy bin is based onthe expected background. In some embodiments, the generating of theprojection vector further includes generating source signature weightedprojection and a background weighted projection and setting theprojection vector to the difference between the source signatureweighted projection and the background weighted projection. In someembodiments, a source signature represents a shielding of the source. Insome embodiments, the method further generates aggregated measurementsof different numbers of measurements and for each aggregatedmeasurement, generates a metric based on the aggregated measurement, theprojection vector, and an expected variance for the aggregatedmeasurement. In some embodiments, the method, when the metric satisfiesa source present threshold, indicates that presence of the source hasbeen detected. In some embodiments, the method, when the metricsatisfies a source present threshold, indicates that presence of thesource has been semi-definitively detected. In some embodiments, thegenerating of the detection metric includes dividing the product of theprojection vector and the measurement by the square root of the expectedvariance.

In some embodiments, one or more computing systems are provided forgenerating a metric relating to a source of radiation in measurements ofradiation. The one or more computing systems comprise one or morecomputer-readable storage mediums storing computer-executableinstructions and one or more processors for executing thecomputer-executable instructions stored in the one or morecomputer-readable storage mediums. When executed, the instructionsaccess a source signature of the source, an estimated background, andbackground basis vectors. The instructions generate a projection vectorbased on the source signature, estimated background, and the backgroundbasis vectors. The instructions access a measurement. The instructionsgenerate the metric based on the measurement, the projection vector, andan expected variance for the measurement. In some embodiments, thesource signature is a histogram representing an energy range dividedinto energy bins, each energy bin having a value representing a count ofphotons emitted by that source over a time interval. In someembodiments, the instructions that generate of the projection vectorgenerate a weighting matrix from the estimated background wherein avariance of each energy bin is based on the expected background. In someembodiments, the instructions that generate the projection vectorfurther generate source signature weighted projection and a backgroundweighted projection and sets the projection vector to the differencebetween the source signature weighted projection and the backgroundweighted projection. In some embodiments, a source signature representsa shielding of the source. In some embodiments, the instructions furthergenerate aggregated measurements of different numbers of measurementsand for each aggregated measurement, generate a metric based on theaggregated measurement, the projection vector, and an expected variancefor the aggregated measurement. In some embodiments, the instructionsfurther, when the metric satisfies a source present threshold, indicatethat presence of the source has been detected. In some embodiments, theinstructions further, when the metric satisfies a source presentthreshold, indicate that presence of the source has beensemi-definitively detected. In some embodiments, the instructions thatgenerates of the detection metric divides the product of the projectionvector and the measurement by the square root of an expected variance.

In some embodiments, a method performed by one or more computing systemsis provide for generating a metric relating to a source of radiation inmeasurements of radiation collected in sequence. The method accesses asource signature of the source, an estimated background, and backgroundbasis vectors. The method generates a projection vector as representedby the following equation:T=S ^(t) W(I−B(B ^(t) WB)⁻¹ B ^(t) W)where T represents the projection vector, S represents the sourcesignature, B represents the estimated background, and W represents thebackground basis vectors. The method accesses a measurement. The methodgenerates the metric by as represented by the following equation:

${DM} = \frac{TX}{\sqrt{{X}_{1}}}$where DM represents the metric, X represents the measurement and |X|₁represents the expected variance for the measurement. In someembodiments, the method detects presence of a source of based on themetric. In some embodiments, the method identifies the source based onthe metric. In some embodiments, the source signature is a histogramrepresenting an energy range divided into energy bins, each energy binhaving a value representing a count of photons emitted by that sourceover a time interval. In some embodiments, a source signature representsa shielding of the source. In some embodiments, the method generatesaggregated measurements of different numbers of measurements and foreach aggregated measurement, generates a metric based on the aggregatedmeasurement, the projection vector, and an expected variance for theaggregated measurement.Hybrid Matched Filter

In some embodiments, a method performed by one or more computing systemsis provided for generating a metric relating to a source of radiation inmeasurements of radiation. The method accesses a source signature of thesource and an estimated background. The method generates a backgroundvector factoring out the source signature. The method generates aprojection vector based on a weighting matrix derived from the estimatedbackground and based on the source signature and the estimatedbackground. The method accesses a measurement. The method generates themetric based on the measurement, the projection vector, and an expectedvariance an average background. In some embodiments, the sourcesignature is a histogram representing an energy range divided intoenergy bins, each energy bin having a value representing a count ofphotons emitted by that source over a time interval. In someembodiments, the weighting matrix is generated from the estimatedbackground wherein a variance of each energy bin is based on theexpected background. In some embodiments, a source signature representsa shielding of the source. In some embodiments, the method generatesaggregated measurements of different numbers of measurements and foreach aggregated measurement, generates a metric based on the aggregatedmeasurement and the projection vector. In some embodiments, when themetric satisfies a source present threshold, the method indicates thatpresence of the source has been detected. In some embodiments, when themetric satisfies a source present threshold, the method indicates thatpresence of the source has been semi-definitively detected.

In some embodiments, one or more computing systems are provided togenerate a metric relating to a source of radiation in measurements ofradiation. The one or more computing systems comprise one or morecomputer-readable storage mediums storing computer-executableinstructions and one or more processors for executing thecomputer-executable instructions stored in the one or morecomputer-readable storage mediums. When executed, the instructionsaccess a source signature of the source and an estimated background. Theinstructions generate a background vector factoring out the sourcesignature. The instructions generate a projection vector based on aweighting matrix derived from the estimated background and based on thesource signature and the estimated background. The instructions access ameasurement. The instructions generate the metric based on themeasurement, the projection vector, and an expected variance an averagebackground. In some embodiments, the source signature is a histogramrepresenting an energy range divided into energy bins, each energy binhaving a value representing a count of photons emitted by that sourceover a time interval. In some embodiments, the weighting matrix isgenerated from the estimated background wherein a variance of eachenergy bin is based on the expected background. In some embodiments, asource signature represents a shielding of the source. In someembodiments, the instructions further generate aggregated measurementsof different numbers of measurements and for each aggregatedmeasurement, generate a metric based on the aggregated measurement andthe projection vector. In some embodiments, the instructions further,when the metric satisfies a source present threshold, indicate thatpresence of the source has been detected. In some embodiments, theinstructions further, when the metric satisfies a source presentthreshold, indicate that presence of the source has beensemi-definitively detected.

In some embodiments, a method performed by one or more computing systemsis provided for generating a metric for indicating presence of a sourceof radiation in measurements of radiation collected in sequence. Themethod accesses a source signature of the source, an estimatedbackground, and background basis vectors. The method generates aprojection vector as represented by the following equation:T=kS ^(t) W(I−{circumflex over (B)}({circumflex over (B)} ^(t)W{circumflex over (B)})⁻¹ {circumflex over (B)} ^(t) W)where T represents the projection vector, S represents the sourcesignature, W represents the basis vectors, {circumflex over (B)} isrepresented by the following equation:{circumflex over (B)}=(I−μS(S ^(t) S)⁻¹ S)and k and μ represents variables adapted to produce a metric with a unitvariance with represented by the following equation:μ=TBwhere B represents the average background. The method access ameasurement. The method generates the metric by as represented by thefollowing equation:

${DM} = \frac{{TX} - {{\mu\Delta}\; t}}{\sqrt{{\overset{\_}{B_{t}}}_{1}}}$

where DM represents the metric, X represents the measurement, trepresents time of the measurement, Δt represents change in time period,and |B_(t)|₁ represents the expected background at time t. In someembodiments, the source signature is a histogram representing an energyrange divided into energy bins, each energy bin having a valuerepresenting a count of photons emitted by that source over a timeinterval. In some embodiments, a source signature represents a shieldingof the source. In some embodiments, the method generates aggregatedmeasurements of different numbers of measurements and for eachaggregated measurement, generating a metric based on the aggregatedmeasurement, the projection vector, and an expected variance for theaggregated measurement.

Bi-Dimensional (“BD”) Matched Filter

In some embodiments, a method performed by one or more computing systemsis provided for generating a metric relating to a source of radiation inmeasurements of radiation. accessing a measurement, a source signatureof the source and an estimated background. The method generates abackground vector factoring out the source signature. The methodgenerates a projection vector based on a weighting matrix derived fromthe estimated background and based on the source signature and theestimated background. The method accesses a measurement. The methodgenerates the metric based on the measurement, the projection vector,and an expected variance an average background. In some embodiments, thesource signature is a histogram representing an energy range dividedinto energy bins, each energy bin having a value representing a count ofphotons emitted by that source over a time interval. In someembodiments, the weighting matrix is generated from the estimatedbackground wherein a variance of each energy bin is based on theexpected background. In some embodiments, a source signature representsa shielding of the source. In some embodiments, the method generatesaggregated measurements of different numbers of measurements and foreach aggregated measurement, generates a metric based on the aggregatedmeasurement and the projection vector. In some embodiments, when themetric satisfies a source present threshold, the method indicates thatpresence of the source has been detected. In some embodiments, when themetric satisfies a source present threshold, the method indicates thatpresence of the source has been semi-definitively detected.

In some embodiments, one or more computing systems are provided forgenerating a metric relating to a source of radiation in measurements ofradiation. The one or more computing systems comprise one or morecomputer-readable storage mediums for storing computer-executableinstructions for controlling the one or more computing systems and oneor more processors for executing the computer-executable instructionsstored in the one or more computer-readable storage mediums. Theinstructions access a measurement, a source signature of the source andan estimated background. The instructions generate a background vectorfactoring out the source signature. The instructions generate aprojection vector based on a weighting matrix derived from the estimatedbackground and based on the source signature and the estimatedbackground. The instructions access a measurement. The instructionsgenerate the metric based on the measurement, the projection vector, andan expected variance an average background. In some embodiments, thesource signature is a histogram representing an energy range dividedinto energy bins, each energy bin having a value representing a count ofphotons emitted by that source over a time interval. In someembodiments, the weighting matrix is generated from the estimatedbackground wherein a variance of each energy bin is based on theexpected background. In some embodiments, a source signature representsa shielding of the source. In some embodiments, the instructions furthergenerate aggregated measurements of different numbers of measurementsand for each aggregated measurement, generate a metric based on theaggregated measurement and the projection vector. In some embodiments,the instructions further, when the metric satisfies a source presentthreshold, indicate that presence of the source has been detected. Insome embodiments, the instructions, when the metric satisfies a sourcepresent threshold, indicating that presence of the source has beensemi-definitively detected.

In some embodiments, a method performed by one or more computing systemsis provided for generating a metric for indicating presence of a sourceof radiation in measurements of radiation collected in sequence. Themethod accesses a source signature of the source, an estimatedbackground, and background basis vectors. The method solves solving thefollowing equation:

${\begin{bmatrix}S & B \\0 & {\lambda\;\overset{\_}{1}}\end{bmatrix}\begin{bmatrix}s \\\overset{\_}{b}\end{bmatrix}} = \begin{bmatrix}X \\{\lambda\; e_{b}}\end{bmatrix}$where S represents the source signature, 1 represents a row vector ofones equal to the number of background components, s represents theestimated counts in the source, b represents a vector of backgroundintensities in each component, B is a matrix of background basisvectors, X is measurement, e_(b) is the estimated total count in thebackground, λ is a tuning parameter based on reliability of theestimated background total counts. The method accesses a measurement.The method generates the metric by as represented by the followingequation:

${DM} = \frac{s - {\kappa\;{\max\left( {{b - e_{b}},0} \right)}}}{\sqrt{e_{b}}}$where DM represents the metric and K represents a penalty for anydifference in the estimated total count in the background and anexpected background total count in the background. In some embodiments,the solving of the equation is an approximation represented by thefollowing equation:

$\begin{bmatrix}s \\b\end{bmatrix} = {X + {e_{b}.}}$In some embodiments, the source signature is a histogram representing anenergy range divided into energy bins, each energy bin having a valuerepresenting a count of photons emitted by that source over a timeinterval. In some embodiments, a source signature represents a shieldingof the source. In some embodiments, the method further generatesaggregated measurements of different numbers of measurements and foreach aggregated measurement, generating a metric based on the aggregatedmeasurement, the projection vector, and an expected variance for theaggregated measurement.LRT Matched Filter

In some embodiments, a method performed by one or more computing systemsis provided for generating a metric relating to a source of radiation inmeasurements of radiation. The method accesses a source signature of thesource and an estimated background. The method generates a firsthypothesis based on the estimated background and an estimated backgroundrate. The method generates a second hypothesis based on the estimatedbackground, the estimated background rate, and the source signature. Themethod generates a first likelihood for the first hypothesis given aPoisson distribution. The method generates a second likelihood for thesecond hypothesis given the Poisson distribution. The method generates aprojection vector based on the first likelihood and the secondlikelihood. The method accesses a measurement. The method generates themetric based on the measurement, the projection vector, and the estimatebackground rate. In some embodiments, the source signature is ahistogram representing an energy range divided into energy bins. Eachenergy bin has a value representing a count of photons emitted by thatsource over a time interval. In some embodiments, a source signaturerepresents a shielding of the source. In some embodiments, the methodfurther generates aggregated measurements of different numbers ofmeasurements and for each aggregated measurement, generates a metricbased on the aggregated measurement and the projection vector. In someembodiments, when the metric satisfies a source present threshold, themethod indicates that presence of the source has been detected. When themetric satisfies a source present threshold, the method indicates thatpresence of the source has been semi-definitively detected.

One or more computing systems are provided for generating a metricrelating to a source of radiation in measurements of radiation. The oneor more computing systems comprise one or more computer-readable storagemediums for storing computer-executable instructions for controlling theone or more computing systems and one or more processors for executingthe computer-executable instructions stored in the one or morecomputer-readable storage mediums. The instructions access a sourcesignature of the source and an estimated background. The instructionsgenerate a first hypothesis based on the estimated background and anestimated background rate. The instructions generate a second hypothesisbased on the estimated background, the estimated background rate, andthe source signature. The instructions generate a first likelihood forthe first hypothesis given a Poisson distribution. The instructionsgenerate a second likelihood for the second hypothesis given the Poissondistribution. The instructions generate a projection vector based on thefirst likelihood and the second likelihood. The instructions access ameasurement. The instructions generate the metric based on themeasurement, the projection vector, and the estimate background rate. Insome embodiments, the source signature is a histogram representing anenergy range divided into energy bins and each energy bin has a valuerepresenting a count of photons emitted by that source over a timeinterval. In some embodiments, a source signature represents a shieldingof the source. In some embodiments, the instructions aggregatemeasurements of different numbers of measurements and for eachaggregated measurement, generating a metric based on the aggregatedmeasurement and the projection vector. In some embodiments, when themetric satisfies a source present threshold, the instructions indicatesthat presence of the source has been detected. In some embodiments, whenthe metric satisfies a source present threshold, the instructionsindicate that presence of the source has been semi-definitivelydetected.

In some embodiments, a method performed by one or more computing systemsis provided for generating a metric for indicating presence of a sourceof radiation in measurements of radiation collected in sequence. Themethod constructs hypotheses represented by the following equations:H ₀ =B _(s) B _(r) ΔtH ₁ =B _(s) B _(r) Δt+S _(s) k√{square root over (B _(r) Δt)}where H₀ and H₁ are the hypotheses, B_(s) represents a background shape,S_(s) represents a source signature shape, B_(r) represents a backgroundrate estimate, Δt represents change time period, and each shape is ahistogram with a total count of 1. The method calculates a likelihood ofeach measurement given Poisson statistics as represented by thefollowing equation:

${P\left( {X❘H} \right)} = \frac{\Pi_{i}{\exp\left( {- h_{i}} \right)}h_{i}^{x_{i}}}{x_{i}!}$where h_(i) represents the i-th element of the hypothesis. The methodgenerates a ratio of the hypotheses as represent by the followingequation:

${{LRT}(X)} = {{{{\log\left( {1 + {\frac{k}{\sqrt{B_{r}\Delta\; t}}\frac{S_{s}}{B_{s}}}} \right)}^{t}X} - \frac{k}{\sqrt{B_{r}\Delta\; t}}} = {{T^{t}X} + {M.}}}$The method generates the metric by as represented by the followingequation:

${DM} = \frac{\left( {{T^{t}X} + {\kappa\; B_{r}\Delta\; t}} \right)}{\sqrt{{vB}_{r}\Delta\; t}}$where DM represents the metric and K represents a bias term for anydifference in the estimated total count in the background and anexpected background total count in the background. In some embodiments,the source signature is a histogram representing an energy range dividedinto energy bins, each energy bin having a value representing a count ofphotons emitted by that source over a time interval. In someembodiments, a source signature represents a shielding of the source. Insome embodiments, the method further generates aggregated measurementsof different numbers of measurements and for each aggregatedmeasurement, generating a metric based on the aggregated measurement,the projection vector, and an expected variance for the aggregatedmeasurement.Rank Average

In some embodiments, a method performed by one or more computing systemsis provided for adjusting measurement counts of measurements to accountfor temporary changes in the measurements. The method accessesmeasurements. For each measurement, the method generates a measurementcount of that measurement. The method generates an upper averagemeasurement count that is an average of the measurement counts higherthan a threshold count. The measurement calculates an expected offsetfor a probabilistic distribution based on the upper average measurementcount and the measurement counts higher than the threshold. For eachmeasurement, the method subtracts the expected offset from themeasurement count for that measurement. In some embodiments, themeasurements are of radiation and the temporary changes are a result ofchanges in background radiation. In some embodiments, the method furtherdetects when the measurements include a source of radiation. In someembodiments, the detecting is performed using a gross count detectionalgorithm. In some embodiments, the upper average measurement count isbased on a probability distribution.

In some embodiments, one or more computing system for adjustingmeasurement counts of measurements to account for temporary changes inthe measurements. The one or more computing system comprise one or morecomputer-readable storage mediums for storing computer-executableinstructions for controlling the one or more computing systems and oneor more processors for executing the computer-executable instructionsstored in the one or more computer-readable storage mediums. For eachmeasurement of a plurality of measurements, the instructions generate ameasurement count of that measurement. The instructions generate anupper average measurement count that is an average of the measurementcounts higher than a threshold count. The instructions calculate anexpected offset for a probabilistic distribution based on the upperaverage measurement count and the measurement counts higher than thethreshold. For each measurement, the instructions subtract the expectedoffset from the measurement count for that measurement. In someembodiments, 7 the measurements are of radiation and the temporarychanges are a result of changes in background radiation. In someembodiments, the instructions further detect when the measurementsinclude a source of radiation. In some embodiments, the detecting isperformed using a gross count detection algorithm. In some embodiments,the upper average measurement count is based on a probabilitydistribution.

Progressive Projection

In some embodiments, a method performed by one or more computing systemsis provided for estimating a background histogram of background countsmeasurement histograms of measurement counts of measurements collectedto detect presence of a source histogram of a source within themeasurements. The method accesses spanning vectors of histograms thatspan a linear combination of prior background histograms derived fromprior measurement histograms. The method calculates a current backgroundhistogram for the sequence of current measurement histograms. The methoddetermines whether the current background histograms have a strongassociation with or a strong disassociation with a spanning vector. Upondetermining that the current background histogram has a strongassociation with or a strong disassociation with a spanning vector, themethod modifies that spanning vector based on the current backgroundhistogram. After modifying that spanning vector, the method adjusts thespanning vectors to increase diversity. In some embodiments, theadjusting attempts to maximize eigenvalues association with acorrelation matrix formed by the spanning vectors. In some embodiments,the method applies a detection algorithm to detect presence of thesource in a target measurement factoring the adjusted spanning vectors.In some embodiments, the source is a source of radiation. In someembodiments, the prior measurement histograms are predefined histograms.In some embodiments, the prior measurement histograms include histogramscollected during a same collection process and prior to collection ofthe sequence of current measurement histogram. In some embodiments, theprior measurement histograms not collecting during the same collectionprocess.

In some embodiments, one or more computing systems are provided forestimating a background histogram of background counts measurementhistograms of measurement counts of measurements collected to detectpresence of a source histogram of a source within the measurements. Theone or more computing systems comprise one or more computer-readablestorage mediums for storing computer-executable instructions forcontrolling the one or more computing systems and one or more processorsfor executing the computer-executable instructions stored in the one ormore computer-readable storage mediums. The instructions access spanningvectors of histograms that span a linear combination of prior backgroundhistograms derived from prior measurement histograms. The instructionscalculate a current background histogram for the sequence of currentmeasurement histograms. The instructions determine whether the currentbackground histograms have a strong association with or a strongdisassociation with a spanning vector. Upon determining that the currentbackground histogram has a strong association with or a strongdisassociation with a spanning vector, the instructions modify thatspanning vector based on the current background histogram. Aftermodifying that spanning vector, the instructions adjust the spanningvectors to increase diversity. In some embodiments, the instructionsthat adjust attempt to maximize eigenvalues association with acorrelation matrix formed by the spanning vectors. In some embodiments,the instructions further apply a detection algorithm to detect presenceof the source in a target measurement factoring the adjusted spanningvectors. In some embodiments, the source is a source of radiation. Insome embodiments, the prior measurement histograms are predefinedhistograms. In some embodiments, the prior measurement histogramsinclude histograms collected during a same collection process and priorto collection of the sequence of current measurement histogram. In someembodiments, the prior measurement histograms not collecting during thesame collection process.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as example forms of implementingthe claims. Accordingly, the invention is not limited except as by theappended claims.

We claim:
 1. One or more computing systems for identifying a source ofradiation, the one or more computing system comprising: one or morecomputer-readable storage mediums storing computer-executableinstructions of: a radiation source detector that: receives measurementsof radiation; for one or more sources, generates a detection metricindicating whether that source is present in the measurements; andevaluates the detection metrics to detect whether a source is present inthe measurements; and a radiation source identifier that, when thepresence of a source in the measurements is detected, for one or moresources, generates an identification metric indicating whether thatsource is present in the measurements; generates a null-hypothesismetric indicating whether no source is present in the measurements basedon analysis of the measurements and the one or more identificationmetrics; and evaluates the one or more identification metrics and thenull-hypothesis metric to identify the source, if any, that is presentin the measurements; and one or more processor for executing thecomputer-executable instructions stored in the one or morecomputer-readable storage mediums.
 2. The one or more computing systemsof claim 1 wherein the radiation source detector generates the detectionmetric for a source based on a detection source signature for thatsource and the radiation identification detector generates theidentification metric for a source based on a plurality ofidentification source signatures for that source.
 3. The one or morecomputing systems of claim 2 wherein the identification sourcessignatures for a source represent that source with different shieldings.4. The one or more computing systems of claim 1 wherein the radiationsource identifier identifies a radiation source class for an identifiedsource.
 5. The one or more computing systems of claim 1 wherein themeasurements are calibrated to account for drift in the detector thatcollected the measurements.
 6. The one or more computing systems ofclaim 1 wherein the radiation source identifier generates an aggregatedmetric for each source.
 7. The one or more computing systems of claim 1wherein the measurements with in a window are aggregated and radiationis detected and identified based on aggregated measurements.
 8. A methodperformed by a computing system for detecting presence of a source ofradiation from a measurement of radiation, the method comprising: forone or more of sources, for one or more detection algorithms, generatinga metric using that detection algorithm, the metric indicatingsimilarity between a current measurement and a source signature for thatsource, that detection algorithm factoring in an estimate of backgroundradiation generated based on prior measurements to the currentmeasurement; and generating an aggregated metric for that source fromone or more metrics for that source; analyzing the aggregated metric forone or more sources to determine whether a source is likely present inthe current measurement; and upon determining that a source is likelypresent in the current measurement, indicating that the presence of asource has been detected.
 9. The method of claim 8 further comprisinggenerating an estimate of background radiation based on priormeasurements excluding prior measurements for which the presence of asource was detected.
 10. The method of claim 9 wherein the estimate ofbackground radiation is a weighted average based on a measurement and aprior estimate of background radiation.
 11. The method of claim 8wherein a source signature is associated with an identification of asource and a shielding of the source.
 12. The method of claim 11 whereinfor each detection algorithm, a metric is generated for each sourcesignature associated with a source.
 13. The method of claim 8 wherein ameasurement is represented by a histogram of energy ranges with a countof photons for each energy range.
 14. The method of claim 8 wherein ametric is generated for each of a plurality of windows measurements,each window including the current measurement and a number of adjacentprior measurement.
 15. The method of claim 8 wherein a detectionalgorithm is associated with coefficients and further comprisingdynamically adjusting the coefficients based on an estimate ofbackground radiation.
 16. The method of claim 8 wherein the analyzing ofthe aggregated metrics includes applying a classifier that is trainedusing training data that includes feature vectors with features thatinclude detection algorithm, a window size, and an aggregated metric anda label for each feature vector indicating whether a source is present.17. The method of claim 16 wherein the feature vectors are generatedfrom collected measurements of radiation.
 18. The method of claim 16wherein the feature vectors are generated from simulated measurements ofradiation.
 19. The method of claim 18 further comprising calibrating thecurrent measurement to account for drift associated with a detector usedto collect the measurements.
 20. A method performed by a computingsystem for identifying a source of radiation from a measurement ofradiation, the method comprising: receiving a current measurement forwhich presence of a source has been detected based on detection sourcesignatures; for each of a plurality of identification source signatures,generating a metric using an identification algorithm, the metricindicating similarity between a current measurement and a sourcesignature for that source, the identification algorithm factoring in anestimate of background radiation generated when detecting the presenceof the source, the identification source signatures being morecomprehensive than the detection source signatures; for each source,generating a source metric based on one or more metrics generated usinga source signature for that source, wherein at least one source metricis generated based on more than one metric; generating a null-hypothesismetric indicating similarity between the current measurement and theestimate of background radiation, the sources and the estimate ofbackground radiation being targets, the source metrics and thenull-hypothesis metric being target metrics; and for each target,generating a target probability representing presence of that target inthe current measurement, the target probability based on the targetmetric for that target.
 21. The method of claim 20 further comprising,for each of a plurality of radiation source classes, generating aradiation source class probability that the current measurementrepresents a source of that radiation source class, the radiation sourceclass probabilities generated based on target probabilities.
 22. Themethod of claim 20 wherein the generating of a target probabilityfactors in prior probabilities for each target.
 23. The method of claim20 wherein the identification algorithm is based on an orthonormalsubspace projection matched filter algorithm.
 24. A method performed bya computing system for generating a metric relating to a source ofradiation in measurements of radiation, the method comprising: accessinga source signature of the source, an estimated background, andbackground basis vectors; generating a projection vector based on thesource signature, estimated background, and the background basisvectors, and accessing a measurement; and generating the metric based onthe measurement, the projection vector, and an expected variance for themeasurement.
 25. The method of claim 24 wherein the source signature isa histogram representing an energy range divided into energy bins, eachenergy bin having a value representing a count of photons emitted bythat source over a time interval.
 26. The method of claim 25 wherein thegenerating of the projection vector includes generating a weightingmatrix from the estimated background wherein a variance of each energybin is based on the expected background.
 27. The method of claim 25wherein the generating of the projection vector further includesgenerating a source signature weighted projection and a backgroundweighted projection and setting the projection vector to the differencebetween the source signature weighted projection and the backgroundweighted projection.
 28. The method of claim 24 wherein a sourcesignature represents a shielding of the source.
 29. The method of claim24 further comprising generating aggregated measurements of differentnumbers of measurements and for each aggregated measurement, generatingthe metric further based on the aggregated measurement, the projectionvector, and an expected variance for the aggregated measurement.
 30. Themethod of claim 24 further comprising when the metric satisfies a sourcepresent threshold, indicating that presence of the source has beendetected.
 31. The method of claim 24 further comprising when the metricsatisfies a source present threshold, indicating that presence of thesource has been semi-definitively detected.
 32. The method of the claim24 wherein the generating of the metric includes dividing the product ofthe projection vector and the measurement by the square root of theexpected variance.
 33. One or more computing system for generating ametric relating to a source of radiation in measurements of radiation,the one or more computing systems comprising: one or morecomputer-readable storage mediums storing computer-executableinstructions for controlling the one or more computing systems to:access a source signature of the source, an estimated background, andbackground basis vectors; generate a projection vector based on thesource signature, estimated background, and the background basisvectors, and access a measurement; and generate the metric based on themeasurement, the projection vector, and an expected variance for themeasurement; one or more processors for executing thecomputer-executable instructions stored in the one or morecomputer-readable storage mediums.
 34. The one or more computing systemsof claim 33 wherein the source signature is a histogram representing anenergy range divided into energy bins, each energy bin having a valuerepresenting a count of photons emitted by that source over a timeinterval.
 35. The one or more computing systems of claim 34 wherein theinstructions that generate of the projection vector generate a weightingmatrix from the estimated background wherein a variance of each energybin is based on the expected background.
 36. The one or more computingsystems of claim 35 wherein the instructions that generate theprojection vector further generate source signature weighted projectionand a background weighted projection and sets the projection vector tothe difference between the source signature weighted projection and thebackground weighted projection.
 37. The one or more computing systems ofclaim 33 wherein a source signature represents a shielding of thesource.
 38. The one or more computing systems of claim 33 wherein theinstructions further generate aggregated measurements of differentnumbers of measurements and for each aggregated measurement, generate ametric based on the aggregated measurement, the projection vector, andan expected variance for the aggregated measurement.
 39. The one or morethe computing systems of claim 33 wherein the instructions further, whenthe metric satisfies a source present threshold, indicate that presenceof the source has been detected.
 40. The one or more the computingsystems of claim 33 wherein the instructions further, when the metricsatisfies a source present threshold, indicate that presence of thesource has been semi-definitively detected.
 41. The one or more thecomputing systems of claim 33 wherein the instructions that generates ofthe detection metric divides the product of the projection vector andthe measurement by the square root of an expected variance.
 42. A methodperformed by a computing system for generating a metric relating to asource of radiation in measurements of radiation collected in sequence,the method comprising: accessing a source signature of the source, anestimated background, and background basis vectors; generating aprojection vector as represented by the following equation:T=S ^(t) W(I−B(B ^(t) WB)⁻¹ B ^(t) W) where T represents the projectionvector, S represents the source signature, B represents the estimatedbackground, and W represents the background basis vectors; and accessinga measurement; and generating the metric by as represented by thefollowing equation: ${DM} = \frac{TX}{\sqrt{{X}_{1}}}$ where DMrepresents the metric, X represents the measurement and |X|1 ₁represents the expected variance for the measurement.
 43. The method ofclaim 42 further comprising detecting presence of a source of based onthe metric.
 44. The method of claim 42 further comprising identifyingthe source based on the metric.
 45. The method of claim 42 wherein thesource signature is a histogram representing an energy range dividedinto energy bins, each energy bin having a value representing a count ofphotons emitted by that source over a time interval.
 46. The method ofclaim 42 wherein a source signature represents a shielding of thesource.
 47. The method of claim 42 further comprising generatingaggregated measurements of different numbers of measurements and foreach aggregated measurement, generating a metric based on the aggregatedmeasurement, the projection vector, and an expected variance for theaggregated measurement.